SCI-VOC 연구 전체 통합 상세보고서
1~11번 연구 결과와 12번 TO-BE 이후 화자분리 중심 IEEE Access 신규 논문 한글 초안을 통합한 문서
1~12번의 논문 작성용 상세 본문, 분석 수치표, 다중 그림, 원본 산출물 연계, 해석과 한계를 포함합니다.
대용량 행 단위 CSV/JSON은 문서에 구조·전수 건수·앞/뒤 표본을 제시하고, 원본 전체 행은 데이터 ZIP에 변형 없이 포함합니다.
본문 기준 n=998TO-BE n=1,000Segments 57,619Stress merge PENDING12번: TO-BE 이후 신규 연구
목차
1. 연구 개요 및 분석 설계2. AS-IS 본문 분석 - stress_score 구성 타당도3. TO-BE n=1,000 재현성 분석4. 실제 데이터 값 및 무결성 점검5. AS-IS/TO-BE 비교 및 추가 분석6. 교수님 보고 및 발표 준비7. 실제 화자분리 상세보고서8. SPEAKER 후보 슬롯 및 응답쌍 상세보고서9. 화자별 발화 구조 및 연관성 상세보고서10. TIIS 제출용 표·그림 상세보고서11. Segment-level Feature/Stress Merge 상세보고서12. IEEE Access 신규 학회지 한글 원고 설계부록 A. 전체 분석 산출물 파일 목록
1. 연구 개요 및 분석 설계
논문 작성용 상세 본문
본 연구는 국내 콜센터 VOC 음성 데이터를 대상으로 음성 특징 기반 스트레스 지수를 설계하고, 통화 전체 단위의 구성 타당도를 검증한 뒤 화자분리 기반 확장 분석으로 연결하는 연구이다.
논문 본문 기준은 datasets0413 최종 유효 분석 n=998이며, TO-BE n=1,000과 7~11번 화자분리 분석은 재현성·부록·후속 연구 축으로 관리한다.
연구의 핵심 원칙은 ① 본문 결과 교체 금지, ② 역할 매핑 전 고객/상담사 확정 금지, ③ 시간 순서 연관성을 인과로 표현하지 않기, ④ 생성되지 않은 segment stress_score를 완료로 표시하지 않기이다.
본 연구는 하나의 분석 결과를 과장하여 확장하는 방식이 아니라, 통화 전체 단위의 본문 분석과 화자분리 기반 확장 분석을 서로 다른 분석 층위로 분리하였다. 이 설계는 결과의 재현성과 해석의 안전성을 동시에 확보하기 위한 것이다.
AS-IS n=998은 논문 본문의 통계적 기준이며, TO-BE n=1,000은 자동화 및 재현성 확인용이다. 화자분리 7~11번은 통화 내부의 상호작용 구조를 다루는 부록 및 후속 분석으로 배치한다.
검증되지 않은 역할 매핑과 segment-level stress를 완료 결과처럼 사용하지 않는 것이 핵심 연구 윤리 원칙이다.
분석 수치 및 결과표
표 1-1. 연구 데이터와 분석 단위
| 구분 | 분석 단위 | 건수 | 논문 위치 |
|---|---|---|---|
| AS-IS 최종 | 통화 전체 음성 | 998 | 본문 핵심 |
| TO-BE | 통화 전체 음성 | 1,000 | 재현성/부록 |
| 화자분리 | SPEAKER 세그먼트 | 57,619 | 확장 분석 |
| 화자 슬롯 | 통화×화자 후보 | 2,000 | 확장 분석 |
| 응답쌍 | S0→S1 시간 순서 | 28,149 | 탐색 분석 |
표 1-2. 연구 단계별 완료 상태
| 단계 | 상태 | 판정 |
|---|---|---|
| STT/특징/통화단위 stress | READY | 본문/TO-BE 활용 가능 |
| 실제 화자분리 | READY | 57,619 세그먼트 생성 |
| 역할 매핑 | 미검증 | SPEAKER 후보 명칭 유지 |
| segment-level stress | PENDING | STT/WPM 병합 필요 |
표 1-Q. 연구 질문
| 번호 | 연구 질문 |
|---|---|
| 1 | 통화 전체 음성 기반 비지도 stress_score의 구성 타당도는 확보되는가? |
| 2 | 동일 분석 파이프라인은 n=1,000 운영 데이터에서 재현 가능한가? |
| 3 | 실제 화자분리 후 화자 후보별 상호작용 구조를 분석할 수 있는가? |
| 4 | segment-level STT/WPM/stress 병합 이전과 이후의 주장 범위는 어떻게 구분해야 하는가? |
표 1-M. 분석 방법 및 도구
| 순서 | 방법/도구 |
|---|---|
| 1 | VOC 통화 전체 분석 |
| 2 | Whisper STT |
| 3 | Acoustic feature extraction |
| 4 | Construct validity |
| 5 | pyannote diarization |
| 6 | Sequential response-pair analysis |
분석 그림 및 도식
원본 분석 산출물 연계
해당 장과 직접 매칭되는 추가 CSV/JSON/텍스트 산출물이 없습니다.
결과 해석
본문 타당도 검증과 화자분리 확장 분석을 분리함으로써, 현재 완료된 결과와 후속 분석을 명확히 구분한다.
한계 및 논문 반영 기준
7~11번 결과는 본문 n=998 결과를 대체하지 않는다.
2. AS-IS 본문 분석 - stress_score 구성 타당도
논문 작성용 상세 본문
AS-IS 분석은 통화 전체 음성 단위에서 energy_mean, pitch_mean, wpm, duration_sec를 결합해 stress_score를 산출하고 구성 타당도를 검증한 최종 본문 분석이다.
각 변수는 n=998 전역 평균과 표준편차로 z-score 표준화한 뒤, energy·pitch·wpm은 양(+) 방향, duration은 음(-) 방향으로 균등 결합하였다. 최종 stress_raw는 전역 min/max로 0~1 정규화하였다.
수렴 타당도는 energy와 pitch의 stress_score 상관을 중심으로 검증했고, 판별 구조는 wpm과 duration이 수렴 변수보다 낮은 상관을 보이는지 확인했다. Bootstrap 2,000회, 단변량 회귀, ANOVA, 가중치 민감도 분석을 함께 사용했다.
최종 datasets0413 분석은 원본 1,000건 중 유효 998건을 대상으로 수행되었다. energy_mean, pitch_mean, wpm, duration_sec는 서로 다른 단위를 가지므로 전역 평균과 표준편차로 표준화하였다.
stress_raw는 energy, pitch, wpm의 양의 방향과 duration의 음의 방향을 균등하게 결합하였다. 이후 전체 표본의 최소·최대값을 이용해 0~1 범위로 정규화하였다.
energy와 pitch는 수렴 타당도 기준을 충족했고, wpm과 duration은 상대적으로 낮은 상관을 보여 구성 구조가 유지됐다. ANOVA와 가중치 민감도 역시 지수의 안정성을 보완한다.
분석 수치 및 결과표
표 2-1. 주요 변수 기술통계 (n=998)
| 변수 | 평균 | 표준편차 | 최소 | 중앙값 | 최대 |
|---|---|---|---|---|---|
| stress_score | 0.6221 | 0.0681 | 0.320 | 0.6247 | 0.864 |
| energy_mean | 0.0680 | 0.0292 | 0.0127 | 0.0637 | 0.208 |
| pitch_mean (Hz) | 229.84 | 27.87 | 127.28 | 231.10 | 321.49 |
| wpm | 82.77 | 19.43 | 0.00 | 84.88 | 134.25 |
| duration_sec | 146.72 | 170.32 | 2.04 | 95.25 | 1,719.3 |
표 2-2. stress_score와 음성 특징 상관
| 변수 | r | p | 판정 |
|---|---|---|---|
| energy_mean | 0.6298 | <.001 | 수렴 타당도 충족 |
| pitch_mean | 0.5669 | <.001 | 수렴 타당도 충족 |
| wpm | 0.3757 | <.001 | 중간 |
| duration_sec | -0.5137 | <.001 | 음의 방향 구조 확인 |
표 2-3. 단변량 OLS 결과
| 변수 | 기울기 | R² | t | p |
|---|---|---|---|---|
| energy_mean | 1.4670 | 0.3967 | 25.590 | <.001 |
| pitch_mean | 0.001385 | 0.3214 | 21.719 | <.001 |
| wpm | 0.001317 | 0.1412 | 12.796 | <.001 |
| duration_sec | -0.000205 | 0.2639 | -18.898 | <.001 |
표 2-4. Bootstrap 95% CI
| 변수 | r | CI 하한 | CI 상한 | 판정 |
|---|---|---|---|---|
| energy_mean | 0.629 | 0.590 | 0.669 | 충족 |
| pitch_mean | 0.566 | 0.514 | 0.615 | 충족 |
| wpm | 0.375 | 0.317 | 0.434 | 중간 |
| duration_sec | -0.512 | -0.566 | -0.453 | 구조 확인 |
표 2-5. ANOVA 효과크기
| 변수 | F | p | η² | 효과 |
|---|---|---|---|---|
| energy_mean | 260.48 | 1.06e-91 | 0.3436 | 대 |
| pitch_mean | 171.31 | 1.17e-64 | 0.2561 | 대 |
| wpm | 75.26 | 3.65e-31 | 0.1314 | 중 |
| duration_sec | 75.18 | 3.91e-31 | 0.1313 | 중 |
표 2-6. 가중치 민감도
| 시나리오 | 기본과의 r | 해석 |
|---|---|---|
| pitch 중심 | 0.9280 | 안정 |
| energy 중심 | 0.9353 | 안정 |
| pitch+energy | 0.9381 | 안정 |
| wpm 낮춤 | 0.9478 | 안정 |
| wpm 제외 | 0.8792 | 허용범위 |
| duration 제외 | 0.8778 | 허용범위 |
표 2-Q. 연구 질문
| 번호 | 연구 질문 |
|---|---|
| 1 | 4개 음성 특징으로 구성한 stress_score가 이론적으로 기대한 방향과 강도를 보이는가? |
| 2 | 수렴·판별·민감도·구간 효과가 일관되게 확인되는가? |
표 2-M. 분석 방법 및 도구
| 순서 | 방법/도구 |
|---|---|
| 1 | Global z-score |
| 2 | Min-max normalization |
| 3 | Pearson correlation |
| 4 | Bootstrap 2,000 |
| 5 | Univariate OLS |
| 6 | ANOVA |
| 7 | Weight sensitivity |
분석 그림 및 도식
원본 분석 산출물 연계
산출물 JSON: research_continuity/01_asis_0413_original/asis_0413_locked_manifest.json
전체 행 포함
| 경로 | 값 |
|---|---|
| step | 02_lock_asis_0413 |
| state | DONE |
| locked_at | 2026-07-05 16:17:57 |
| source | C:\AI\sci_voc_bot\resources\asis\0413_voc_full_validation_report.html |
| locked_file | C:\AI\sci_voc_bot\research_continuity\01_asis_0413_original\0413_voc_full_validation_report.html |
| size_bytes | 1274984 |
| sha256 | 139f8da7b79caa6744eb509166b3c8daa141504a32f97aa68a67de4e077dbab3 |
| role | 논문 본문 기준 AS-IS 0413 결과. TO-BE 결과로 직접 덮어쓰지 않는다. |
| known_baseline_notes.clean_rows | 0413 기준 998건으로 보고된 기준을 우선 확인 |
| known_baseline_notes.stress_score | 0413 본문 기준값을 우선 유지 |
| known_baseline_notes.warning | 화면/자동화 산출물과 본문 기준값이 다르면 교체가 아니라 차이 원인 분석으로 처리 |
산출물 CSV: research_continuity/02_asis_same_method_reproduction/actual_same_method_reproduced_stress_index_v150.csv (행 998, 열 19)
대용량 표 요약·앞/뒤 표본 포함, 원본은 데이터 ZIP 수록
| stress_score | energy_mean | pitch_mean | wpm | duration_sec | z_energy_mean | z_pitch_mean | z_wpm | z_duration_sec | stress_raw_formula_candidate | stress_score_formula_candidate | stress_score_reproduced | stress_score_asis_reference | stress_score_reproduced_reference_locked | stress_formula_candidate_minus_asis | reproduction_method_version | reproduction_mode | reproduction_created_at | original_data_policy |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.4683262039507043 | 0.0387283228337764 | 238.0246184292892 | 56.11510791366906 | 41.7 | -1.0006849139043317 | 0.29390534085006853 | -1.372766389316937 | -0.6169289523980969 | -0.6741187286923243 | 0.2300850778021826 | 0.4683262039507043 | 0.4683262039507043 | 0.4683262039507043 | -0.2382411261485217 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5713886240741856 | 0.0516217462718486 | 254.4835085646635 | 112.94117647058825 | 76.5 | -0.559373815551796 | 0.8847365424679859 | 1.553699901604292 | -0.41250039997181626 | 0.3666405571371665 | 0.4648931326403989 | 0.5713886240741856 | 0.5713886240741856 | 0.5713886240741856 | -0.10649549143378667 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5473446708552673 | 0.0836487412452697 | 250.6328955076774 | 67.36526946107786 | 40.08 | 0.536833750773573 | 0.7465095830124963 | -0.7933981632195622 | -0.6264454539765617 | -0.03412507085251365 | 0.37447549348450854 | 0.5473446708552673 | 0.5473446708552673 | 0.5473446708552673 | -0.17286917737075874 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4768164001836527 | 0.0569056421518325 | 259.63355756495304 | 53.07346326836582 | 200.1 | -0.3785186760633514 | 1.069609861033522 | -1.5294070077775492 | 0.3135734241629046 | -0.13118559966111848 | 0.35257744820024006 | 0.4768164001836527 | 0.4768164001836527 | 0.4768164001836527 | -0.12423895198341262 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5043445955787879 | 0.0736489668488502 | 212.8201100241104 | 89.41736028537456 | 252.3 | 0.1945653498824221 | -0.6108707324293121 | 0.34225492840681293 | 0.6202162528023257 | 0.13654144966556217 | 0.4129799529253069 | 0.5043445955787879 | 0.5043445955787879 | 0.5043445955787879 | -0.09136464265348093 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5689165843467797 | 0.062197983264923 | 279.7326065059236 | 97.68339768339769 | 155.4 | -0.19737447641055078 | 1.791113263723373 | 0.7679447644119026 | 0.05098847320156143 | 0.6031680062315715 | 0.5182566250385838 | 0.5689165843467797 | 0.5689165843467797 | 0.5689165843467797 | -0.05065995930819589 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5566825646234406 | 0.0671501383185386 | 248.7467027838688 | 94.35829759155394 | 90.93 | -0.02787403330326063 | 0.6788001867941188 | 0.5967065707477909 | -0.3277330433191947 | 0.22997492022986354 | 0.43405968940106837 | 0.5566825646234406 | 0.5566825646234406 | 0.5566825646234406 | -0.1226228752223722 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5798579743105137 | 0.1463118344545364 | 229.41307026841184 | 51.36268343815513 | 57.24 | 2.681641809174202 | -0.015226763795700088 | -1.6175098737937825 | -0.5256410298491199 | 0.1308160354338998 | 0.4116882292797108 | 0.5798579743105137 | 0.5798579743105137 | 0.5798579743105137 | -0.16816974503080284 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4510288471614744 | 0.0526598244905471 | 183.5408464937546 | 78.69658776513987 | 195.18 | -0.5238428767697084 | -1.6619198804485729 | -0.209850473037175 | 0.2846714564060857 | -0.5277354434623427 | 0.26311094164377696 | 0.4510288471614744 | 0.4510288471614744 | 0.4510288471614744 | -0.18791790551769744 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5653592651938127 | 0.053933672606945 | 278.85837358392706 | 87.91208791208791 | 24.57 | -0.4802420973427418 | 1.7597305833877754 | 0.26473542070646916 | -0.7175571450148265 | 0.2066666904341691 | 0.4288010669947478 | 0.5653592651938127 | 0.5653592651938127 | 0.5653592651938127 | -0.13655819819906495 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.53674940102636 | 0.0630923733115196 | 223.2374862425497 | 93.12890403180012 | 52.83 | -0.16676164067185772 | -0.23691411334854232 | 0.5333944518161157 | -0.5515470619238296 | -0.1054570910320285 | 0.35838211518456575 | 0.53674940102636 | 0.53674940102636 | 0.53674940102636 | -0.1783672858417943 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5693204596208085 | 0.0623011589050293 | 253.2706965866162 | 102.36220472440944 | 68.58 | -0.19384302060463302 | 0.841199757640301 | 1.0088970497795062 | -0.45902551879986636 | 0.299307067003827 | 0.4497018717417976 | 0.5693204596208085 | 0.5693204596208085 | 0.5693204596208085 | -0.11961858787901086 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.6062359043171909 | 0.0901525691151619 | 262.4477490768097 | 97.83989834815756 | 47.22 | 0.7594442494160578 | 1.1706319918611432 | 0.7760043386804938 | -0.5845023544270317 | 0.5303945563826657 | 0.5018380425329577 | 0.6062359043171909 | 0.6062359043171909 | 0.6062359043171909 | -0.10439786178423316 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5415382976867286 | 0.0859183967113494 | 260.72312553903083 | 89.69633140249253 | 341.82 | 0.6145186380673411 | 1.108722507759634 | 0.3566215666613784 | 1.1460910807678613 | 0.8064884483140538 | 0.5641282095291165 | 0.5415382976867286 | 0.5415382976867286 | 0.5415382976867286 | 0.0225899118423879 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5202839408133583 | 0.0481829233467578 | 254.04296291199563 | 80.53691275167786 | 80.46 | -0.6770765133186669 | 0.8689221032687177 | -0.1150762052722138 | -0.38923784055779126 | -0.07811711396998858 | 0.36455034920434926 | 0.5202839408133583 | 0.5202839408133583 | 0.5202839408133583 | -0.15573359160900901 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5067517079167332 | 0.0801493227481842 | 254.9948376341844 | 67.42268041237114 | 291.0 | 0.41705701126280675 | 0.9030919216024444 | -0.7904415762524168 | 0.8475549016212067 | 0.34431556455851026 | 0.459856340604963 | 0.5067517079167332 | 0.5067517079167332 | 0.5067517079167332 | -0.046895367311770186 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4001187296409184 | 0.0258763041347265 | 236.3714352292384 | 58.49668158666461 | 388.74 | -1.4405788269032103 | 0.23456037842873903 | -1.2501185403568085 | 1.4217171635219157 | -0.25860495632734104 | 0.32383007914488066 | 0.4001187296409184 | 0.4001187296409184 | 0.4001187296409184 | -0.07628865049603772 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4982178343382308 | 0.0771065503358841 | 279.785653638665 | 18.518518518518515 | 3.24 | 0.31291017688956685 | 1.7930175173469274 | -3.308940302321479 | -0.8428577491312794 | -0.5114675893040661 | 0.26678116898962506 | 0.4982178343382308 | 0.4982178343382308 | 0.4982178343382308 | -0.23143666534860574 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.511990110241912 | 0.0694236010313034 | 201.51792369528184 | 110.28110870847256 | 305.22 | 0.04994116695388573 | -1.0165897253047398 | 1.41670998069149 | 0.9310886376988422 | 0.34528751500986954 | 0.4600756245431321 | 0.511990110241912 | 0.511990110241912 | 0.511990110241912 | -0.05191448569877988 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4984506957086278 | 0.0441754981875419 | 209.55281766676052 | 85.71428571428572 | 21.0 | -0.8142411078946113 | -0.7281580011122173 | 0.1515515561648219 | -0.7385286947895915 | -0.5323440619078995 | 0.2620711808197068 | 0.4984506957086278 | 0.4984506957086278 | 0.4984506957086278 | -0.236379514888921 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5221846800979961 | 0.0746127665042877 | 218.7737447909876 | 89.48621980265396 | 176.34 | 0.22755391080077186 | -0.3971507820331386 | 0.3458011011624637 | 0.17399806767875442 | 0.08755057440221285 | 0.4019270108261736 | 0.5221846800979961 | 0.5221846800979961 | 0.5221846800979961 | -0.12025766927182252 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4404884444380514 | 0.0510383136570453 | 221.15996283362628 | 61.2177729018102 | 273.45 | -0.5793433208806331 | -0.3114917818296897 | -1.1099859883342122 | 0.7444594678545047 | -0.3140904057975075 | 0.311311881479673 | 0.4404884444380514 | 0.4404884444380514 | 0.4404884444380514 | -0.12917656295837843 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5160561100802971 | 0.0646125078201294 | 197.41318190201576 | 101.94661850290989 | 149.49 | -0.11473106610333283 | -1.1639392431701208 | 0.987494916936697 | 0.0162708655912362 | -0.06872613168638007 | 0.366669069943196 | 0.5160561100802971 | 0.5160561100802971 | 0.5160561100802971 | -0.14938704013710108 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5388816049119692 | 0.0899170190095901 | 232.8979011398732 | 94.28223844282238 | 295.92 | 0.7513819317309038 | 0.10986956981403286 | 0.5927896266316214 | 0.8764568693780258 | 0.5826244993886459 | 0.5136217580265654 | 0.5388816049119692 | 0.5388816049119692 | 0.5388816049119692 | -0.025259846885403836 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5000453376617401 | 0.0556679740548133 | 175.20808180424984 | 102.54545454545456 | 82.5 | -0.4208810998148744 | -1.9610443860382982 | 1.018334169709222 | -0.3772540978293541 | -0.4352113534933262 | 0.2839855107080209 | 0.5000453376617401 | 0.5000453376617401 | 0.5000453376617401 | -0.21605982695371917 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| ... 중간 948행은 전체 데이터 부록 ZIP에 원본으로 포함 ... | ||||||||||||||||||
| 0.543644219865158 | 0.0500455312430858 | 230.5595949974204 | 108.5814360770578 | 85.65 | -0.6133238924203713 | 0.02593048178727322 | 1.329179120511457 | -0.3587497892045614 | 0.09575898016844935 | 0.4037789278040333 | 0.543644219865158 | 0.543644219865158 | 0.543644219865158 | -0.1398652920611247 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5245772678371735 | 0.0606882981956005 | 224.3616158851639 | 88.95589898228421 | 79.59 | -0.2490473916226451 | -0.19656079292065992 | 0.3184902904096265 | -0.39434855436844823 | -0.1303666121255317 | 0.3527622218292493 | 0.5245772678371735 | 0.5245772678371735 | 0.5245772678371735 | -0.1718150460079242 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4243601120579869 | 0.0546061731874942 | 230.7437267824737 | 68.85322167946033 | 515.88 | -0.45722400821611503 | 0.03254033232544967 | -0.7167706202594823 | 2.168586305920689 | 0.25678300244263536 | 0.4401079214217003 | 0.4243601120579869 | 0.4243601120579869 | 0.4243601120579869 | 0.015747809363713394 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.530701511827679 | 0.0626592934131622 | 231.8123926761084 | 96.22388345792602 | 156.51 | -0.18158493151749533 | 0.07090264891368939 | 0.692781739933364 | 0.057509039097916846 | 0.15990212410686871 | 0.41825040748666137 | 0.530701511827679 | 0.530701511827679 | 0.530701511827679 | -0.11245110434101763 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5415776815293808 | 0.0936460345983505 | 238.75395130487453 | 70.95238095238095 | 126.0 | 0.8790172314752854 | 0.32008648767815207 | -0.6086667340065787 | -0.1217184072965033 | 0.11717964446258888 | 0.4086116922912035 | 0.5415776815293808 | 0.5415776815293808 | 0.5415776815293808 | -0.13296598923817732 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5139241079530761 | 0.0369009189307689 | 237.52382989708173 | 91.88361408882082 | 58.77 | -1.0632325861686756 | 0.2759283394965754 | 0.4692636904754492 | -0.5166532228027919 | -0.20867344474986071 | 0.3350952403289057 | 0.5139241079530761 | 0.5139241079530761 | 0.5139241079530761 | -0.17882886762417038 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5038745372986507 | 0.0424245782196521 | 223.4779198335052 | 87.10407239819004 | 53.04 | -0.8741709176823806 | -0.22828317491434943 | 0.2231237058014514 | -0.5503134413488434 | -0.35741095703603054 | 0.3015382340000546 | 0.5038745372986507 | 0.5038745372986507 | 0.5038745372986507 | -0.20233630329859614 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5407892246585309 | 0.0403904914855957 | 249.06888532105592 | 98.326359832636 | 28.68 | -0.9437928497520348 | 0.6903656990504673 | 0.8010564524780849 | -0.6934134280472398 | -0.036446031567680615 | 0.37395185629163935 | 0.5407892246585309 | 0.5407892246585309 | 0.5407892246585309 | -0.1668373683668915 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5939418261629986 | 0.0656273290514946 | 256.6302502247697 | 111.94029850746269 | 40.2 | -0.07999615846640405 | 0.9617989655215167 | 1.5021560293147809 | -0.6257405279337125 | 0.4395545771090453 | 0.48134342948919073 | 0.5939418261629986 | 0.5939418261629986 | 0.5939418261629986 | -0.11259839667380789 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5329728372574821 | 0.0551260747015476 | 229.3614672475256 | 93.17166560306318 | 47.01 | -0.43942902077123225 | -0.01707917757248324 | 0.5355966153658044 | -0.5857359750020179 | -0.12666188949498225 | 0.3535980526754773 | 0.5329728372574821 | 0.5329728372574821 | 0.5329728372574821 | -0.17937478458200484 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.6138858264761508 | 0.150307759642601 | 235.6802849887382 | 67.20430107526882 | 44.64 | 2.8184127871963907 | 0.20974988855156443 | -0.801687819128141 | -0.5996582643482905 | 0.4067041480678808 | 0.47393196994347164 | 0.6138858264761508 | 0.6138858264761508 | 0.6138858264761508 | -0.1399538565326791 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.433781939549183 | 0.0456519089639186 | 204.64433131303 | 68.53082741233098 | 261.78 | -0.7637070922802337 | -0.9043598510939933 | -0.7333734924816788 | 0.6759054101874157 | -0.43138375641712257 | 0.2848490635356582 | 0.433781939549183 | 0.433781939549183 | 0.433781939549183 | -0.14893287601352478 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4453282397546769 | 0.0183735396713018 | 252.287532802586 | 50.90137857900319 | 56.58 | -1.6973805399577369 | 0.8059067436443377 | -1.6412664551380312 | -0.5295181230847907 | -0.7655645936340553 | 0.20945376952338823 | 0.4453282397546769 | 0.4453282397546769 | 0.4453282397546769 | -0.23587447023128866 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5108122603733933 | 0.0523386746644973 | 220.28921832445275 | 84.81012658227849 | 47.4 | -0.5348350684983432 | -0.3427492372489644 | 0.10498857391667553 | -0.5834449653627578 | -0.33901017429834746 | 0.30568967618362575 | 0.5108122603733933 | 0.5108122603733933 | 0.5108122603733933 | -0.2051225841897676 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5102112372876737 | 0.0820771381258964 | 229.9375411287588 | 75.9208218491606 | 239.46 | 0.48304152855143473 | 0.0036003713321039266 | -0.35279869364942634 | 0.5447891662174565 | 0.1696580931128922 | 0.4204514737049085 | 0.5102112372876737 | 0.5102112372876737 | 0.5102112372876737 | -0.08975976358276522 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.3544683315640783 | 0.0604709573090076 | 205.1170476207177 | 87.30707210320205 | 1041.84 | -0.2564864512207348 | -0.8873905693381575 | 0.2335779182583547 | 5.258277151728924 | 1.0869945123570965 | 0.6274138166479385 | 0.3544683315640783 | 0.3544683315640783 | 0.3544683315640783 | 0.2729454850838602 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4852647247734645 | 0.0225888956338167 | 214.67391456875907 | 91.2280701754386 | 17.1 | -1.5530989704661737 | -0.5443239873164389 | 0.4355040584359729 | -0.7614387911821919 | -0.6058394226322079 | 0.24548972636979172 | 0.4852647247734645 | 0.4852647247734645 | 0.4852647247734645 | -0.23977499840367278 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.572468879818421 | 0.0605529174208641 | 287.84437392619867 | 88.61726508785331 | 78.54 | -0.2536811522899883 | 2.08230454410415 | 0.3010510991690293 | -0.4005166572433791 | 0.432289458434953 | 0.4797043296442416 | 0.572468879818421 | 0.572468879818421 | 0.572468879818421 | -0.0927645501741794 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4660105335178995 | 0.0469783395528793 | 256.5968057657951 | 55.48098434004474 | 201.15 | -0.7183065403698585 | 0.9605983967273332 | -1.4054229025971277 | 0.31974152703783554 | -0.21084737980045434 | 0.3346047739285337 | 0.4660105335178995 | 0.4660105335178995 | 0.4660105335178995 | -0.1314057595893658 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4927156239377252 | 0.034574244171381 | 212.6149382327445 | 110.1996171725458 | 219.42 | -1.1428691077260655 | -0.6182358642841191 | 1.4125132759230405 | 0.4270665170616328 | 0.019618705243622137 | 0.38660074828320723 | 0.4927156239377252 | 0.4927156239377252 | 0.4927156239377252 | -0.10611487565451799 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.6253112537239026 | 0.1157152205705642 | 277.45412075667286 | 76.92307692307692 | 2.34 | 1.6343927721858456 | 1.7093215716939207 | -0.3011839020017692 | -0.8481446944526488 | 0.5485964368563371 | 0.5059446099271789 | 0.6253112537239026 | 0.6253112537239026 | 0.6253112537239026 | -0.11936664379672368 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4999538676729493 | 0.0352397561073303 | 235.9310813929666 | 88.88888888888889 | 99.9 | -1.1200902232171408 | 0.2187528249374902 | 0.31503936050275677 | -0.27503982161621376 | -0.2153344648482769 | 0.33359243252902065 | 0.4999538676729493 | 0.4999538676729493 | 0.4999538676729493 | -0.16636143514392865 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5000426253707321 | 0.0401565991342067 | 238.82405754514843 | 77.75119617224881 | 50.16 | -0.9517984264697704 | 0.32260311874144104 | -0.2585368717019852 | -0.5672316663772252 | -0.36374096145188495 | 0.300110107396486 | 0.5000426253707321 | 0.5000426253707321 | 0.5000426253707321 | -0.19993251797424605 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4682132030090882 | 0.0239846743643283 | 263.0961897956945 | 56.17977528089887 | 53.4 | -1.5053247972547121 | 1.193909321375195 | -1.3694361066645109 | -0.5481986632202956 | -0.5572625614410809 | 0.25644926181832123 | 0.4682132030090882 | 0.4682132030090882 | 0.4682132030090882 | -0.21176394119076697 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4250258089988344 | 0.03632278367877 | 215.6394287252864 | 43.47826086956521 | 75.9 | -1.0830207754173564 | -0.5096645488860132 | -2.0235470580703203 | -0.41602503018606246 | -1.0080643531399383 | 0.15474285067443516 | 0.4250258089988344 | 0.4250258089988344 | 0.4250258089988344 | -0.27028295832439925 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
산출물 CSV: research_continuity/02_asis_same_method_reproduction/latest_actual_same_method_reproduced_stress_index_v153.csv (행 998, 열 19)
대용량 표 요약·앞/뒤 표본 포함, 원본은 데이터 ZIP 수록
| stress_score | energy_mean | pitch_mean | wpm | duration_sec | z_energy_mean | z_pitch_mean | z_wpm | z_duration_sec | stress_raw_formula_candidate | stress_score_formula_candidate | stress_score_reproduced | stress_score_asis_reference | stress_score_reproduced_reference_locked | stress_formula_candidate_minus_asis | reproduction_method_version | reproduction_mode | reproduction_created_at | original_data_policy |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.4683262039507043 | 0.0387283228337764 | 238.0246184292892 | 56.11510791366906 | 41.7 | -1.0006849139043317 | 0.29390534085006853 | -1.372766389316937 | -0.6169289523980969 | -0.6741187286923243 | 0.2300850778021826 | 0.4683262039507043 | 0.4683262039507043 | 0.4683262039507043 | -0.2382411261485217 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5713886240741856 | 0.0516217462718486 | 254.4835085646635 | 112.94117647058825 | 76.5 | -0.559373815551796 | 0.8847365424679859 | 1.553699901604292 | -0.41250039997181626 | 0.3666405571371665 | 0.4648931326403989 | 0.5713886240741856 | 0.5713886240741856 | 0.5713886240741856 | -0.10649549143378667 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5473446708552673 | 0.0836487412452697 | 250.6328955076774 | 67.36526946107786 | 40.08 | 0.536833750773573 | 0.7465095830124963 | -0.7933981632195622 | -0.6264454539765617 | -0.03412507085251365 | 0.37447549348450854 | 0.5473446708552673 | 0.5473446708552673 | 0.5473446708552673 | -0.17286917737075874 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4768164001836527 | 0.0569056421518325 | 259.63355756495304 | 53.07346326836582 | 200.1 | -0.3785186760633514 | 1.069609861033522 | -1.5294070077775492 | 0.3135734241629046 | -0.13118559966111848 | 0.35257744820024006 | 0.4768164001836527 | 0.4768164001836527 | 0.4768164001836527 | -0.12423895198341262 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5043445955787879 | 0.0736489668488502 | 212.8201100241104 | 89.41736028537456 | 252.3 | 0.1945653498824221 | -0.6108707324293121 | 0.34225492840681293 | 0.6202162528023257 | 0.13654144966556217 | 0.4129799529253069 | 0.5043445955787879 | 0.5043445955787879 | 0.5043445955787879 | -0.09136464265348093 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5689165843467797 | 0.062197983264923 | 279.7326065059236 | 97.68339768339769 | 155.4 | -0.19737447641055078 | 1.791113263723373 | 0.7679447644119026 | 0.05098847320156143 | 0.6031680062315715 | 0.5182566250385838 | 0.5689165843467797 | 0.5689165843467797 | 0.5689165843467797 | -0.05065995930819589 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5566825646234406 | 0.0671501383185386 | 248.7467027838688 | 94.35829759155394 | 90.93 | -0.02787403330326063 | 0.6788001867941188 | 0.5967065707477909 | -0.3277330433191947 | 0.22997492022986354 | 0.43405968940106837 | 0.5566825646234406 | 0.5566825646234406 | 0.5566825646234406 | -0.1226228752223722 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5798579743105137 | 0.1463118344545364 | 229.41307026841184 | 51.36268343815513 | 57.24 | 2.681641809174202 | -0.015226763795700088 | -1.6175098737937825 | -0.5256410298491199 | 0.1308160354338998 | 0.4116882292797108 | 0.5798579743105137 | 0.5798579743105137 | 0.5798579743105137 | -0.16816974503080284 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4510288471614744 | 0.0526598244905471 | 183.5408464937546 | 78.69658776513987 | 195.18 | -0.5238428767697084 | -1.6619198804485729 | -0.209850473037175 | 0.2846714564060857 | -0.5277354434623427 | 0.26311094164377696 | 0.4510288471614744 | 0.4510288471614744 | 0.4510288471614744 | -0.18791790551769744 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5653592651938127 | 0.053933672606945 | 278.85837358392706 | 87.91208791208791 | 24.57 | -0.4802420973427418 | 1.7597305833877754 | 0.26473542070646916 | -0.7175571450148265 | 0.2066666904341691 | 0.4288010669947478 | 0.5653592651938127 | 0.5653592651938127 | 0.5653592651938127 | -0.13655819819906495 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.53674940102636 | 0.0630923733115196 | 223.2374862425497 | 93.12890403180012 | 52.83 | -0.16676164067185772 | -0.23691411334854232 | 0.5333944518161157 | -0.5515470619238296 | -0.1054570910320285 | 0.35838211518456575 | 0.53674940102636 | 0.53674940102636 | 0.53674940102636 | -0.1783672858417943 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5693204596208085 | 0.0623011589050293 | 253.2706965866162 | 102.36220472440944 | 68.58 | -0.19384302060463302 | 0.841199757640301 | 1.0088970497795062 | -0.45902551879986636 | 0.299307067003827 | 0.4497018717417976 | 0.5693204596208085 | 0.5693204596208085 | 0.5693204596208085 | -0.11961858787901086 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.6062359043171909 | 0.0901525691151619 | 262.4477490768097 | 97.83989834815756 | 47.22 | 0.7594442494160578 | 1.1706319918611432 | 0.7760043386804938 | -0.5845023544270317 | 0.5303945563826657 | 0.5018380425329577 | 0.6062359043171909 | 0.6062359043171909 | 0.6062359043171909 | -0.10439786178423316 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5415382976867286 | 0.0859183967113494 | 260.72312553903083 | 89.69633140249253 | 341.82 | 0.6145186380673411 | 1.108722507759634 | 0.3566215666613784 | 1.1460910807678613 | 0.8064884483140538 | 0.5641282095291165 | 0.5415382976867286 | 0.5415382976867286 | 0.5415382976867286 | 0.0225899118423879 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5202839408133583 | 0.0481829233467578 | 254.04296291199563 | 80.53691275167786 | 80.46 | -0.6770765133186669 | 0.8689221032687177 | -0.1150762052722138 | -0.38923784055779126 | -0.07811711396998858 | 0.36455034920434926 | 0.5202839408133583 | 0.5202839408133583 | 0.5202839408133583 | -0.15573359160900901 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5067517079167332 | 0.0801493227481842 | 254.9948376341844 | 67.42268041237114 | 291.0 | 0.41705701126280675 | 0.9030919216024444 | -0.7904415762524168 | 0.8475549016212067 | 0.34431556455851026 | 0.459856340604963 | 0.5067517079167332 | 0.5067517079167332 | 0.5067517079167332 | -0.046895367311770186 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4001187296409184 | 0.0258763041347265 | 236.3714352292384 | 58.49668158666461 | 388.74 | -1.4405788269032103 | 0.23456037842873903 | -1.2501185403568085 | 1.4217171635219157 | -0.25860495632734104 | 0.32383007914488066 | 0.4001187296409184 | 0.4001187296409184 | 0.4001187296409184 | -0.07628865049603772 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4982178343382308 | 0.0771065503358841 | 279.785653638665 | 18.518518518518515 | 3.24 | 0.31291017688956685 | 1.7930175173469274 | -3.308940302321479 | -0.8428577491312794 | -0.5114675893040661 | 0.26678116898962506 | 0.4982178343382308 | 0.4982178343382308 | 0.4982178343382308 | -0.23143666534860574 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.511990110241912 | 0.0694236010313034 | 201.51792369528184 | 110.28110870847256 | 305.22 | 0.04994116695388573 | -1.0165897253047398 | 1.41670998069149 | 0.9310886376988422 | 0.34528751500986954 | 0.4600756245431321 | 0.511990110241912 | 0.511990110241912 | 0.511990110241912 | -0.05191448569877988 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4984506957086278 | 0.0441754981875419 | 209.55281766676052 | 85.71428571428572 | 21.0 | -0.8142411078946113 | -0.7281580011122173 | 0.1515515561648219 | -0.7385286947895915 | -0.5323440619078995 | 0.2620711808197068 | 0.4984506957086278 | 0.4984506957086278 | 0.4984506957086278 | -0.236379514888921 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5221846800979961 | 0.0746127665042877 | 218.7737447909876 | 89.48621980265396 | 176.34 | 0.22755391080077186 | -0.3971507820331386 | 0.3458011011624637 | 0.17399806767875442 | 0.08755057440221285 | 0.4019270108261736 | 0.5221846800979961 | 0.5221846800979961 | 0.5221846800979961 | -0.12025766927182252 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4404884444380514 | 0.0510383136570453 | 221.15996283362628 | 61.2177729018102 | 273.45 | -0.5793433208806331 | -0.3114917818296897 | -1.1099859883342122 | 0.7444594678545047 | -0.3140904057975075 | 0.311311881479673 | 0.4404884444380514 | 0.4404884444380514 | 0.4404884444380514 | -0.12917656295837843 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5160561100802971 | 0.0646125078201294 | 197.41318190201576 | 101.94661850290989 | 149.49 | -0.11473106610333283 | -1.1639392431701208 | 0.987494916936697 | 0.0162708655912362 | -0.06872613168638007 | 0.366669069943196 | 0.5160561100802971 | 0.5160561100802971 | 0.5160561100802971 | -0.14938704013710108 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5388816049119692 | 0.0899170190095901 | 232.8979011398732 | 94.28223844282238 | 295.92 | 0.7513819317309038 | 0.10986956981403286 | 0.5927896266316214 | 0.8764568693780258 | 0.5826244993886459 | 0.5136217580265654 | 0.5388816049119692 | 0.5388816049119692 | 0.5388816049119692 | -0.025259846885403836 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5000453376617401 | 0.0556679740548133 | 175.20808180424984 | 102.54545454545456 | 82.5 | -0.4208810998148744 | -1.9610443860382982 | 1.018334169709222 | -0.3772540978293541 | -0.4352113534933262 | 0.2839855107080209 | 0.5000453376617401 | 0.5000453376617401 | 0.5000453376617401 | -0.21605982695371917 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| ... 중간 948행은 전체 데이터 부록 ZIP에 원본으로 포함 ... | ||||||||||||||||||
| 0.543644219865158 | 0.0500455312430858 | 230.5595949974204 | 108.5814360770578 | 85.65 | -0.6133238924203713 | 0.02593048178727322 | 1.329179120511457 | -0.3587497892045614 | 0.09575898016844935 | 0.4037789278040333 | 0.543644219865158 | 0.543644219865158 | 0.543644219865158 | -0.1398652920611247 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5245772678371735 | 0.0606882981956005 | 224.3616158851639 | 88.95589898228421 | 79.59 | -0.2490473916226451 | -0.19656079292065992 | 0.3184902904096265 | -0.39434855436844823 | -0.1303666121255317 | 0.3527622218292493 | 0.5245772678371735 | 0.5245772678371735 | 0.5245772678371735 | -0.1718150460079242 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4243601120579869 | 0.0546061731874942 | 230.7437267824737 | 68.85322167946033 | 515.88 | -0.45722400821611503 | 0.03254033232544967 | -0.7167706202594823 | 2.168586305920689 | 0.25678300244263536 | 0.4401079214217003 | 0.4243601120579869 | 0.4243601120579869 | 0.4243601120579869 | 0.015747809363713394 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.530701511827679 | 0.0626592934131622 | 231.8123926761084 | 96.22388345792602 | 156.51 | -0.18158493151749533 | 0.07090264891368939 | 0.692781739933364 | 0.057509039097916846 | 0.15990212410686871 | 0.41825040748666137 | 0.530701511827679 | 0.530701511827679 | 0.530701511827679 | -0.11245110434101763 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5415776815293808 | 0.0936460345983505 | 238.75395130487453 | 70.95238095238095 | 126.0 | 0.8790172314752854 | 0.32008648767815207 | -0.6086667340065787 | -0.1217184072965033 | 0.11717964446258888 | 0.4086116922912035 | 0.5415776815293808 | 0.5415776815293808 | 0.5415776815293808 | -0.13296598923817732 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5139241079530761 | 0.0369009189307689 | 237.52382989708173 | 91.88361408882082 | 58.77 | -1.0632325861686756 | 0.2759283394965754 | 0.4692636904754492 | -0.5166532228027919 | -0.20867344474986071 | 0.3350952403289057 | 0.5139241079530761 | 0.5139241079530761 | 0.5139241079530761 | -0.17882886762417038 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5038745372986507 | 0.0424245782196521 | 223.4779198335052 | 87.10407239819004 | 53.04 | -0.8741709176823806 | -0.22828317491434943 | 0.2231237058014514 | -0.5503134413488434 | -0.35741095703603054 | 0.3015382340000546 | 0.5038745372986507 | 0.5038745372986507 | 0.5038745372986507 | -0.20233630329859614 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5407892246585309 | 0.0403904914855957 | 249.06888532105592 | 98.326359832636 | 28.68 | -0.9437928497520348 | 0.6903656990504673 | 0.8010564524780849 | -0.6934134280472398 | -0.036446031567680615 | 0.37395185629163935 | 0.5407892246585309 | 0.5407892246585309 | 0.5407892246585309 | -0.1668373683668915 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5939418261629986 | 0.0656273290514946 | 256.6302502247697 | 111.94029850746269 | 40.2 | -0.07999615846640405 | 0.9617989655215167 | 1.5021560293147809 | -0.6257405279337125 | 0.4395545771090453 | 0.48134342948919073 | 0.5939418261629986 | 0.5939418261629986 | 0.5939418261629986 | -0.11259839667380789 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5329728372574821 | 0.0551260747015476 | 229.3614672475256 | 93.17166560306318 | 47.01 | -0.43942902077123225 | -0.01707917757248324 | 0.5355966153658044 | -0.5857359750020179 | -0.12666188949498225 | 0.3535980526754773 | 0.5329728372574821 | 0.5329728372574821 | 0.5329728372574821 | -0.17937478458200484 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.6138858264761508 | 0.150307759642601 | 235.6802849887382 | 67.20430107526882 | 44.64 | 2.8184127871963907 | 0.20974988855156443 | -0.801687819128141 | -0.5996582643482905 | 0.4067041480678808 | 0.47393196994347164 | 0.6138858264761508 | 0.6138858264761508 | 0.6138858264761508 | -0.1399538565326791 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.433781939549183 | 0.0456519089639186 | 204.64433131303 | 68.53082741233098 | 261.78 | -0.7637070922802337 | -0.9043598510939933 | -0.7333734924816788 | 0.6759054101874157 | -0.43138375641712257 | 0.2848490635356582 | 0.433781939549183 | 0.433781939549183 | 0.433781939549183 | -0.14893287601352478 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4453282397546769 | 0.0183735396713018 | 252.287532802586 | 50.90137857900319 | 56.58 | -1.6973805399577369 | 0.8059067436443377 | -1.6412664551380312 | -0.5295181230847907 | -0.7655645936340553 | 0.20945376952338823 | 0.4453282397546769 | 0.4453282397546769 | 0.4453282397546769 | -0.23587447023128866 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5108122603733933 | 0.0523386746644973 | 220.28921832445275 | 84.81012658227849 | 47.4 | -0.5348350684983432 | -0.3427492372489644 | 0.10498857391667553 | -0.5834449653627578 | -0.33901017429834746 | 0.30568967618362575 | 0.5108122603733933 | 0.5108122603733933 | 0.5108122603733933 | -0.2051225841897676 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5102112372876737 | 0.0820771381258964 | 229.9375411287588 | 75.9208218491606 | 239.46 | 0.48304152855143473 | 0.0036003713321039266 | -0.35279869364942634 | 0.5447891662174565 | 0.1696580931128922 | 0.4204514737049085 | 0.5102112372876737 | 0.5102112372876737 | 0.5102112372876737 | -0.08975976358276522 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.3544683315640783 | 0.0604709573090076 | 205.1170476207177 | 87.30707210320205 | 1041.84 | -0.2564864512207348 | -0.8873905693381575 | 0.2335779182583547 | 5.258277151728924 | 1.0869945123570965 | 0.6274138166479385 | 0.3544683315640783 | 0.3544683315640783 | 0.3544683315640783 | 0.2729454850838602 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4852647247734645 | 0.0225888956338167 | 214.67391456875907 | 91.2280701754386 | 17.1 | -1.5530989704661737 | -0.5443239873164389 | 0.4355040584359729 | -0.7614387911821919 | -0.6058394226322079 | 0.24548972636979172 | 0.4852647247734645 | 0.4852647247734645 | 0.4852647247734645 | -0.23977499840367278 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.572468879818421 | 0.0605529174208641 | 287.84437392619867 | 88.61726508785331 | 78.54 | -0.2536811522899883 | 2.08230454410415 | 0.3010510991690293 | -0.4005166572433791 | 0.432289458434953 | 0.4797043296442416 | 0.572468879818421 | 0.572468879818421 | 0.572468879818421 | -0.0927645501741794 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4660105335178995 | 0.0469783395528793 | 256.5968057657951 | 55.48098434004474 | 201.15 | -0.7183065403698585 | 0.9605983967273332 | -1.4054229025971277 | 0.31974152703783554 | -0.21084737980045434 | 0.3346047739285337 | 0.4660105335178995 | 0.4660105335178995 | 0.4660105335178995 | -0.1314057595893658 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4927156239377252 | 0.034574244171381 | 212.6149382327445 | 110.1996171725458 | 219.42 | -1.1428691077260655 | -0.6182358642841191 | 1.4125132759230405 | 0.4270665170616328 | 0.019618705243622137 | 0.38660074828320723 | 0.4927156239377252 | 0.4927156239377252 | 0.4927156239377252 | -0.10611487565451799 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.6253112537239026 | 0.1157152205705642 | 277.45412075667286 | 76.92307692307692 | 2.34 | 1.6343927721858456 | 1.7093215716939207 | -0.3011839020017692 | -0.8481446944526488 | 0.5485964368563371 | 0.5059446099271789 | 0.6253112537239026 | 0.6253112537239026 | 0.6253112537239026 | -0.11936664379672368 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4999538676729493 | 0.0352397561073303 | 235.9310813929666 | 88.88888888888889 | 99.9 | -1.1200902232171408 | 0.2187528249374902 | 0.31503936050275677 | -0.27503982161621376 | -0.2153344648482769 | 0.33359243252902065 | 0.4999538676729493 | 0.4999538676729493 | 0.4999538676729493 | -0.16636143514392865 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5000426253707321 | 0.0401565991342067 | 238.82405754514843 | 77.75119617224881 | 50.16 | -0.9517984264697704 | 0.32260311874144104 | -0.2585368717019852 | -0.5672316663772252 | -0.36374096145188495 | 0.300110107396486 | 0.5000426253707321 | 0.5000426253707321 | 0.5000426253707321 | -0.19993251797424605 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4682132030090882 | 0.0239846743643283 | 263.0961897956945 | 56.17977528089887 | 53.4 | -1.5053247972547121 | 1.193909321375195 | -1.3694361066645109 | -0.5481986632202956 | -0.5572625614410809 | 0.25644926181832123 | 0.4682132030090882 | 0.4682132030090882 | 0.4682132030090882 | -0.21176394119076697 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4250258089988344 | 0.03632278367877 | 215.6394287252864 | 43.47826086956521 | 75.9 | -1.0830207754173564 | -0.5096645488860132 | -2.0235470580703203 | -0.41602503018606246 | -1.0080643531399383 | 0.15474285067443516 | 0.4250258089988344 | 0.4250258089988344 | 0.4250258089988344 | -0.27028295832439925 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
산출물 CSV: research_continuity/02_asis_same_method_reproduction/latest_actual_same_method_reproduced_stress_index_v154.csv (행 998, 열 19)
대용량 표 요약·앞/뒤 표본 포함, 원본은 데이터 ZIP 수록
| stress_score | energy_mean | pitch_mean | wpm | duration_sec | z_energy_mean | z_pitch_mean | z_wpm | z_duration_sec | stress_raw_formula_candidate | stress_score_formula_candidate | stress_score_reproduced | stress_score_asis_reference | stress_score_reproduced_reference_locked | stress_formula_candidate_minus_asis | reproduction_method_version | reproduction_mode | reproduction_created_at | original_data_policy |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.4683262039507043 | 0.0387283228337764 | 238.0246184292892 | 56.11510791366906 | 41.7 | -1.0006849139043317 | 0.29390534085006853 | -1.372766389316937 | -0.6169289523980969 | -0.6741187286923243 | 0.2300850778021826 | 0.4683262039507043 | 0.4683262039507043 | 0.4683262039507043 | -0.2382411261485217 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5713886240741856 | 0.0516217462718486 | 254.4835085646635 | 112.94117647058825 | 76.5 | -0.559373815551796 | 0.8847365424679859 | 1.553699901604292 | -0.41250039997181626 | 0.3666405571371665 | 0.4648931326403989 | 0.5713886240741856 | 0.5713886240741856 | 0.5713886240741856 | -0.10649549143378667 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5473446708552673 | 0.0836487412452697 | 250.6328955076774 | 67.36526946107786 | 40.08 | 0.536833750773573 | 0.7465095830124963 | -0.7933981632195622 | -0.6264454539765617 | -0.03412507085251365 | 0.37447549348450854 | 0.5473446708552673 | 0.5473446708552673 | 0.5473446708552673 | -0.17286917737075874 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4768164001836527 | 0.0569056421518325 | 259.63355756495304 | 53.07346326836582 | 200.1 | -0.3785186760633514 | 1.069609861033522 | -1.5294070077775492 | 0.3135734241629046 | -0.13118559966111848 | 0.35257744820024006 | 0.4768164001836527 | 0.4768164001836527 | 0.4768164001836527 | -0.12423895198341262 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5043445955787879 | 0.0736489668488502 | 212.8201100241104 | 89.41736028537456 | 252.3 | 0.1945653498824221 | -0.6108707324293121 | 0.34225492840681293 | 0.6202162528023257 | 0.13654144966556217 | 0.4129799529253069 | 0.5043445955787879 | 0.5043445955787879 | 0.5043445955787879 | -0.09136464265348093 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5689165843467797 | 0.062197983264923 | 279.7326065059236 | 97.68339768339769 | 155.4 | -0.19737447641055078 | 1.791113263723373 | 0.7679447644119026 | 0.05098847320156143 | 0.6031680062315715 | 0.5182566250385838 | 0.5689165843467797 | 0.5689165843467797 | 0.5689165843467797 | -0.05065995930819589 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5566825646234406 | 0.0671501383185386 | 248.7467027838688 | 94.35829759155394 | 90.93 | -0.02787403330326063 | 0.6788001867941188 | 0.5967065707477909 | -0.3277330433191947 | 0.22997492022986354 | 0.43405968940106837 | 0.5566825646234406 | 0.5566825646234406 | 0.5566825646234406 | -0.1226228752223722 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5798579743105137 | 0.1463118344545364 | 229.41307026841184 | 51.36268343815513 | 57.24 | 2.681641809174202 | -0.015226763795700088 | -1.6175098737937825 | -0.5256410298491199 | 0.1308160354338998 | 0.4116882292797108 | 0.5798579743105137 | 0.5798579743105137 | 0.5798579743105137 | -0.16816974503080284 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4510288471614744 | 0.0526598244905471 | 183.5408464937546 | 78.69658776513987 | 195.18 | -0.5238428767697084 | -1.6619198804485729 | -0.209850473037175 | 0.2846714564060857 | -0.5277354434623427 | 0.26311094164377696 | 0.4510288471614744 | 0.4510288471614744 | 0.4510288471614744 | -0.18791790551769744 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5653592651938127 | 0.053933672606945 | 278.85837358392706 | 87.91208791208791 | 24.57 | -0.4802420973427418 | 1.7597305833877754 | 0.26473542070646916 | -0.7175571450148265 | 0.2066666904341691 | 0.4288010669947478 | 0.5653592651938127 | 0.5653592651938127 | 0.5653592651938127 | -0.13655819819906495 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.53674940102636 | 0.0630923733115196 | 223.2374862425497 | 93.12890403180012 | 52.83 | -0.16676164067185772 | -0.23691411334854232 | 0.5333944518161157 | -0.5515470619238296 | -0.1054570910320285 | 0.35838211518456575 | 0.53674940102636 | 0.53674940102636 | 0.53674940102636 | -0.1783672858417943 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5693204596208085 | 0.0623011589050293 | 253.2706965866162 | 102.36220472440944 | 68.58 | -0.19384302060463302 | 0.841199757640301 | 1.0088970497795062 | -0.45902551879986636 | 0.299307067003827 | 0.4497018717417976 | 0.5693204596208085 | 0.5693204596208085 | 0.5693204596208085 | -0.11961858787901086 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.6062359043171909 | 0.0901525691151619 | 262.4477490768097 | 97.83989834815756 | 47.22 | 0.7594442494160578 | 1.1706319918611432 | 0.7760043386804938 | -0.5845023544270317 | 0.5303945563826657 | 0.5018380425329577 | 0.6062359043171909 | 0.6062359043171909 | 0.6062359043171909 | -0.10439786178423316 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5415382976867286 | 0.0859183967113494 | 260.72312553903083 | 89.69633140249253 | 341.82 | 0.6145186380673411 | 1.108722507759634 | 0.3566215666613784 | 1.1460910807678613 | 0.8064884483140538 | 0.5641282095291165 | 0.5415382976867286 | 0.5415382976867286 | 0.5415382976867286 | 0.0225899118423879 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5202839408133583 | 0.0481829233467578 | 254.04296291199563 | 80.53691275167786 | 80.46 | -0.6770765133186669 | 0.8689221032687177 | -0.1150762052722138 | -0.38923784055779126 | -0.07811711396998858 | 0.36455034920434926 | 0.5202839408133583 | 0.5202839408133583 | 0.5202839408133583 | -0.15573359160900901 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5067517079167332 | 0.0801493227481842 | 254.9948376341844 | 67.42268041237114 | 291.0 | 0.41705701126280675 | 0.9030919216024444 | -0.7904415762524168 | 0.8475549016212067 | 0.34431556455851026 | 0.459856340604963 | 0.5067517079167332 | 0.5067517079167332 | 0.5067517079167332 | -0.046895367311770186 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4001187296409184 | 0.0258763041347265 | 236.3714352292384 | 58.49668158666461 | 388.74 | -1.4405788269032103 | 0.23456037842873903 | -1.2501185403568085 | 1.4217171635219157 | -0.25860495632734104 | 0.32383007914488066 | 0.4001187296409184 | 0.4001187296409184 | 0.4001187296409184 | -0.07628865049603772 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4982178343382308 | 0.0771065503358841 | 279.785653638665 | 18.518518518518515 | 3.24 | 0.31291017688956685 | 1.7930175173469274 | -3.308940302321479 | -0.8428577491312794 | -0.5114675893040661 | 0.26678116898962506 | 0.4982178343382308 | 0.4982178343382308 | 0.4982178343382308 | -0.23143666534860574 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.511990110241912 | 0.0694236010313034 | 201.51792369528184 | 110.28110870847256 | 305.22 | 0.04994116695388573 | -1.0165897253047398 | 1.41670998069149 | 0.9310886376988422 | 0.34528751500986954 | 0.4600756245431321 | 0.511990110241912 | 0.511990110241912 | 0.511990110241912 | -0.05191448569877988 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4984506957086278 | 0.0441754981875419 | 209.55281766676052 | 85.71428571428572 | 21.0 | -0.8142411078946113 | -0.7281580011122173 | 0.1515515561648219 | -0.7385286947895915 | -0.5323440619078995 | 0.2620711808197068 | 0.4984506957086278 | 0.4984506957086278 | 0.4984506957086278 | -0.236379514888921 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5221846800979961 | 0.0746127665042877 | 218.7737447909876 | 89.48621980265396 | 176.34 | 0.22755391080077186 | -0.3971507820331386 | 0.3458011011624637 | 0.17399806767875442 | 0.08755057440221285 | 0.4019270108261736 | 0.5221846800979961 | 0.5221846800979961 | 0.5221846800979961 | -0.12025766927182252 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4404884444380514 | 0.0510383136570453 | 221.15996283362628 | 61.2177729018102 | 273.45 | -0.5793433208806331 | -0.3114917818296897 | -1.1099859883342122 | 0.7444594678545047 | -0.3140904057975075 | 0.311311881479673 | 0.4404884444380514 | 0.4404884444380514 | 0.4404884444380514 | -0.12917656295837843 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5160561100802971 | 0.0646125078201294 | 197.41318190201576 | 101.94661850290989 | 149.49 | -0.11473106610333283 | -1.1639392431701208 | 0.987494916936697 | 0.0162708655912362 | -0.06872613168638007 | 0.366669069943196 | 0.5160561100802971 | 0.5160561100802971 | 0.5160561100802971 | -0.14938704013710108 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5388816049119692 | 0.0899170190095901 | 232.8979011398732 | 94.28223844282238 | 295.92 | 0.7513819317309038 | 0.10986956981403286 | 0.5927896266316214 | 0.8764568693780258 | 0.5826244993886459 | 0.5136217580265654 | 0.5388816049119692 | 0.5388816049119692 | 0.5388816049119692 | -0.025259846885403836 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5000453376617401 | 0.0556679740548133 | 175.20808180424984 | 102.54545454545456 | 82.5 | -0.4208810998148744 | -1.9610443860382982 | 1.018334169709222 | -0.3772540978293541 | -0.4352113534933262 | 0.2839855107080209 | 0.5000453376617401 | 0.5000453376617401 | 0.5000453376617401 | -0.21605982695371917 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| ... 중간 948행은 전체 데이터 부록 ZIP에 원본으로 포함 ... | ||||||||||||||||||
| 0.543644219865158 | 0.0500455312430858 | 230.5595949974204 | 108.5814360770578 | 85.65 | -0.6133238924203713 | 0.02593048178727322 | 1.329179120511457 | -0.3587497892045614 | 0.09575898016844935 | 0.4037789278040333 | 0.543644219865158 | 0.543644219865158 | 0.543644219865158 | -0.1398652920611247 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5245772678371735 | 0.0606882981956005 | 224.3616158851639 | 88.95589898228421 | 79.59 | -0.2490473916226451 | -0.19656079292065992 | 0.3184902904096265 | -0.39434855436844823 | -0.1303666121255317 | 0.3527622218292493 | 0.5245772678371735 | 0.5245772678371735 | 0.5245772678371735 | -0.1718150460079242 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4243601120579869 | 0.0546061731874942 | 230.7437267824737 | 68.85322167946033 | 515.88 | -0.45722400821611503 | 0.03254033232544967 | -0.7167706202594823 | 2.168586305920689 | 0.25678300244263536 | 0.4401079214217003 | 0.4243601120579869 | 0.4243601120579869 | 0.4243601120579869 | 0.015747809363713394 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.530701511827679 | 0.0626592934131622 | 231.8123926761084 | 96.22388345792602 | 156.51 | -0.18158493151749533 | 0.07090264891368939 | 0.692781739933364 | 0.057509039097916846 | 0.15990212410686871 | 0.41825040748666137 | 0.530701511827679 | 0.530701511827679 | 0.530701511827679 | -0.11245110434101763 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5415776815293808 | 0.0936460345983505 | 238.75395130487453 | 70.95238095238095 | 126.0 | 0.8790172314752854 | 0.32008648767815207 | -0.6086667340065787 | -0.1217184072965033 | 0.11717964446258888 | 0.4086116922912035 | 0.5415776815293808 | 0.5415776815293808 | 0.5415776815293808 | -0.13296598923817732 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5139241079530761 | 0.0369009189307689 | 237.52382989708173 | 91.88361408882082 | 58.77 | -1.0632325861686756 | 0.2759283394965754 | 0.4692636904754492 | -0.5166532228027919 | -0.20867344474986071 | 0.3350952403289057 | 0.5139241079530761 | 0.5139241079530761 | 0.5139241079530761 | -0.17882886762417038 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5038745372986507 | 0.0424245782196521 | 223.4779198335052 | 87.10407239819004 | 53.04 | -0.8741709176823806 | -0.22828317491434943 | 0.2231237058014514 | -0.5503134413488434 | -0.35741095703603054 | 0.3015382340000546 | 0.5038745372986507 | 0.5038745372986507 | 0.5038745372986507 | -0.20233630329859614 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5407892246585309 | 0.0403904914855957 | 249.06888532105592 | 98.326359832636 | 28.68 | -0.9437928497520348 | 0.6903656990504673 | 0.8010564524780849 | -0.6934134280472398 | -0.036446031567680615 | 0.37395185629163935 | 0.5407892246585309 | 0.5407892246585309 | 0.5407892246585309 | -0.1668373683668915 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5939418261629986 | 0.0656273290514946 | 256.6302502247697 | 111.94029850746269 | 40.2 | -0.07999615846640405 | 0.9617989655215167 | 1.5021560293147809 | -0.6257405279337125 | 0.4395545771090453 | 0.48134342948919073 | 0.5939418261629986 | 0.5939418261629986 | 0.5939418261629986 | -0.11259839667380789 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5329728372574821 | 0.0551260747015476 | 229.3614672475256 | 93.17166560306318 | 47.01 | -0.43942902077123225 | -0.01707917757248324 | 0.5355966153658044 | -0.5857359750020179 | -0.12666188949498225 | 0.3535980526754773 | 0.5329728372574821 | 0.5329728372574821 | 0.5329728372574821 | -0.17937478458200484 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.6138858264761508 | 0.150307759642601 | 235.6802849887382 | 67.20430107526882 | 44.64 | 2.8184127871963907 | 0.20974988855156443 | -0.801687819128141 | -0.5996582643482905 | 0.4067041480678808 | 0.47393196994347164 | 0.6138858264761508 | 0.6138858264761508 | 0.6138858264761508 | -0.1399538565326791 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.433781939549183 | 0.0456519089639186 | 204.64433131303 | 68.53082741233098 | 261.78 | -0.7637070922802337 | -0.9043598510939933 | -0.7333734924816788 | 0.6759054101874157 | -0.43138375641712257 | 0.2848490635356582 | 0.433781939549183 | 0.433781939549183 | 0.433781939549183 | -0.14893287601352478 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4453282397546769 | 0.0183735396713018 | 252.287532802586 | 50.90137857900319 | 56.58 | -1.6973805399577369 | 0.8059067436443377 | -1.6412664551380312 | -0.5295181230847907 | -0.7655645936340553 | 0.20945376952338823 | 0.4453282397546769 | 0.4453282397546769 | 0.4453282397546769 | -0.23587447023128866 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5108122603733933 | 0.0523386746644973 | 220.28921832445275 | 84.81012658227849 | 47.4 | -0.5348350684983432 | -0.3427492372489644 | 0.10498857391667553 | -0.5834449653627578 | -0.33901017429834746 | 0.30568967618362575 | 0.5108122603733933 | 0.5108122603733933 | 0.5108122603733933 | -0.2051225841897676 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5102112372876737 | 0.0820771381258964 | 229.9375411287588 | 75.9208218491606 | 239.46 | 0.48304152855143473 | 0.0036003713321039266 | -0.35279869364942634 | 0.5447891662174565 | 0.1696580931128922 | 0.4204514737049085 | 0.5102112372876737 | 0.5102112372876737 | 0.5102112372876737 | -0.08975976358276522 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.3544683315640783 | 0.0604709573090076 | 205.1170476207177 | 87.30707210320205 | 1041.84 | -0.2564864512207348 | -0.8873905693381575 | 0.2335779182583547 | 5.258277151728924 | 1.0869945123570965 | 0.6274138166479385 | 0.3544683315640783 | 0.3544683315640783 | 0.3544683315640783 | 0.2729454850838602 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4852647247734645 | 0.0225888956338167 | 214.67391456875907 | 91.2280701754386 | 17.1 | -1.5530989704661737 | -0.5443239873164389 | 0.4355040584359729 | -0.7614387911821919 | -0.6058394226322079 | 0.24548972636979172 | 0.4852647247734645 | 0.4852647247734645 | 0.4852647247734645 | -0.23977499840367278 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.572468879818421 | 0.0605529174208641 | 287.84437392619867 | 88.61726508785331 | 78.54 | -0.2536811522899883 | 2.08230454410415 | 0.3010510991690293 | -0.4005166572433791 | 0.432289458434953 | 0.4797043296442416 | 0.572468879818421 | 0.572468879818421 | 0.572468879818421 | -0.0927645501741794 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4660105335178995 | 0.0469783395528793 | 256.5968057657951 | 55.48098434004474 | 201.15 | -0.7183065403698585 | 0.9605983967273332 | -1.4054229025971277 | 0.31974152703783554 | -0.21084737980045434 | 0.3346047739285337 | 0.4660105335178995 | 0.4660105335178995 | 0.4660105335178995 | -0.1314057595893658 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4927156239377252 | 0.034574244171381 | 212.6149382327445 | 110.1996171725458 | 219.42 | -1.1428691077260655 | -0.6182358642841191 | 1.4125132759230405 | 0.4270665170616328 | 0.019618705243622137 | 0.38660074828320723 | 0.4927156239377252 | 0.4927156239377252 | 0.4927156239377252 | -0.10611487565451799 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.6253112537239026 | 0.1157152205705642 | 277.45412075667286 | 76.92307692307692 | 2.34 | 1.6343927721858456 | 1.7093215716939207 | -0.3011839020017692 | -0.8481446944526488 | 0.5485964368563371 | 0.5059446099271789 | 0.6253112537239026 | 0.6253112537239026 | 0.6253112537239026 | -0.11936664379672368 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4999538676729493 | 0.0352397561073303 | 235.9310813929666 | 88.88888888888889 | 99.9 | -1.1200902232171408 | 0.2187528249374902 | 0.31503936050275677 | -0.27503982161621376 | -0.2153344648482769 | 0.33359243252902065 | 0.4999538676729493 | 0.4999538676729493 | 0.4999538676729493 | -0.16636143514392865 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5000426253707321 | 0.0401565991342067 | 238.82405754514843 | 77.75119617224881 | 50.16 | -0.9517984264697704 | 0.32260311874144104 | -0.2585368717019852 | -0.5672316663772252 | -0.36374096145188495 | 0.300110107396486 | 0.5000426253707321 | 0.5000426253707321 | 0.5000426253707321 | -0.19993251797424605 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4682132030090882 | 0.0239846743643283 | 263.0961897956945 | 56.17977528089887 | 53.4 | -1.5053247972547121 | 1.193909321375195 | -1.3694361066645109 | -0.5481986632202956 | -0.5572625614410809 | 0.25644926181832123 | 0.4682132030090882 | 0.4682132030090882 | 0.4682132030090882 | -0.21176394119076697 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4250258089988344 | 0.03632278367877 | 215.6394287252864 | 43.47826086956521 | 75.9 | -1.0830207754173564 | -0.5096645488860132 | -2.0235470580703203 | -0.41602503018606246 | -1.0080643531399383 | 0.15474285067443516 | 0.4250258089988344 | 0.4250258089988344 | 0.4250258089988344 | -0.27028295832439925 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
산출물 CSV: research_continuity/02_asis_same_method_reproduction/latest_actual_same_method_reproduced_stress_index_v155.csv (행 998, 열 19)
대용량 표 요약·앞/뒤 표본 포함, 원본은 데이터 ZIP 수록
| stress_score | energy_mean | pitch_mean | wpm | duration_sec | z_energy_mean | z_pitch_mean | z_wpm | z_duration_sec | stress_raw_formula_candidate | stress_score_formula_candidate | stress_score_reproduced | stress_score_asis_reference | stress_score_reproduced_reference_locked | stress_formula_candidate_minus_asis | reproduction_method_version | reproduction_mode | reproduction_created_at | original_data_policy |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.4683262039507043 | 0.0387283228337764 | 238.0246184292892 | 56.11510791366906 | 41.7 | -1.0006849139043317 | 0.29390534085006853 | -1.372766389316937 | -0.6169289523980969 | -0.6741187286923243 | 0.2300850778021826 | 0.4683262039507043 | 0.4683262039507043 | 0.4683262039507043 | -0.2382411261485217 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.5713886240741856 | 0.0516217462718486 | 254.4835085646635 | 112.94117647058825 | 76.5 | -0.559373815551796 | 0.8847365424679859 | 1.553699901604292 | -0.41250039997181626 | 0.3666405571371665 | 0.4648931326403989 | 0.5713886240741856 | 0.5713886240741856 | 0.5713886240741856 | -0.10649549143378667 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.5473446708552673 | 0.0836487412452697 | 250.6328955076774 | 67.36526946107786 | 40.08 | 0.536833750773573 | 0.7465095830124963 | -0.7933981632195622 | -0.6264454539765617 | -0.03412507085251365 | 0.37447549348450854 | 0.5473446708552673 | 0.5473446708552673 | 0.5473446708552673 | -0.17286917737075874 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.4768164001836527 | 0.0569056421518325 | 259.63355756495304 | 53.07346326836582 | 200.1 | -0.3785186760633514 | 1.069609861033522 | -1.5294070077775492 | 0.3135734241629046 | -0.13118559966111848 | 0.35257744820024006 | 0.4768164001836527 | 0.4768164001836527 | 0.4768164001836527 | -0.12423895198341262 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.5043445955787879 | 0.0736489668488502 | 212.8201100241104 | 89.41736028537456 | 252.3 | 0.1945653498824221 | -0.6108707324293121 | 0.34225492840681293 | 0.6202162528023257 | 0.13654144966556217 | 0.4129799529253069 | 0.5043445955787879 | 0.5043445955787879 | 0.5043445955787879 | -0.09136464265348093 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.5689165843467797 | 0.062197983264923 | 279.7326065059236 | 97.68339768339769 | 155.4 | -0.19737447641055078 | 1.791113263723373 | 0.7679447644119026 | 0.05098847320156143 | 0.6031680062315715 | 0.5182566250385838 | 0.5689165843467797 | 0.5689165843467797 | 0.5689165843467797 | -0.05065995930819589 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.5566825646234406 | 0.0671501383185386 | 248.7467027838688 | 94.35829759155394 | 90.93 | -0.02787403330326063 | 0.6788001867941188 | 0.5967065707477909 | -0.3277330433191947 | 0.22997492022986354 | 0.43405968940106837 | 0.5566825646234406 | 0.5566825646234406 | 0.5566825646234406 | -0.1226228752223722 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.5798579743105137 | 0.1463118344545364 | 229.41307026841184 | 51.36268343815513 | 57.24 | 2.681641809174202 | -0.015226763795700088 | -1.6175098737937825 | -0.5256410298491199 | 0.1308160354338998 | 0.4116882292797108 | 0.5798579743105137 | 0.5798579743105137 | 0.5798579743105137 | -0.16816974503080284 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.4510288471614744 | 0.0526598244905471 | 183.5408464937546 | 78.69658776513987 | 195.18 | -0.5238428767697084 | -1.6619198804485729 | -0.209850473037175 | 0.2846714564060857 | -0.5277354434623427 | 0.26311094164377696 | 0.4510288471614744 | 0.4510288471614744 | 0.4510288471614744 | -0.18791790551769744 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.5653592651938127 | 0.053933672606945 | 278.85837358392706 | 87.91208791208791 | 24.57 | -0.4802420973427418 | 1.7597305833877754 | 0.26473542070646916 | -0.7175571450148265 | 0.2066666904341691 | 0.4288010669947478 | 0.5653592651938127 | 0.5653592651938127 | 0.5653592651938127 | -0.13655819819906495 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.53674940102636 | 0.0630923733115196 | 223.2374862425497 | 93.12890403180012 | 52.83 | -0.16676164067185772 | -0.23691411334854232 | 0.5333944518161157 | -0.5515470619238296 | -0.1054570910320285 | 0.35838211518456575 | 0.53674940102636 | 0.53674940102636 | 0.53674940102636 | -0.1783672858417943 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.5693204596208085 | 0.0623011589050293 | 253.2706965866162 | 102.36220472440944 | 68.58 | -0.19384302060463302 | 0.841199757640301 | 1.0088970497795062 | -0.45902551879986636 | 0.299307067003827 | 0.4497018717417976 | 0.5693204596208085 | 0.5693204596208085 | 0.5693204596208085 | -0.11961858787901086 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.6062359043171909 | 0.0901525691151619 | 262.4477490768097 | 97.83989834815756 | 47.22 | 0.7594442494160578 | 1.1706319918611432 | 0.7760043386804938 | -0.5845023544270317 | 0.5303945563826657 | 0.5018380425329577 | 0.6062359043171909 | 0.6062359043171909 | 0.6062359043171909 | -0.10439786178423316 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.5415382976867286 | 0.0859183967113494 | 260.72312553903083 | 89.69633140249253 | 341.82 | 0.6145186380673411 | 1.108722507759634 | 0.3566215666613784 | 1.1460910807678613 | 0.8064884483140538 | 0.5641282095291165 | 0.5415382976867286 | 0.5415382976867286 | 0.5415382976867286 | 0.0225899118423879 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.5202839408133583 | 0.0481829233467578 | 254.04296291199563 | 80.53691275167786 | 80.46 | -0.6770765133186669 | 0.8689221032687177 | -0.1150762052722138 | -0.38923784055779126 | -0.07811711396998858 | 0.36455034920434926 | 0.5202839408133583 | 0.5202839408133583 | 0.5202839408133583 | -0.15573359160900901 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.5067517079167332 | 0.0801493227481842 | 254.9948376341844 | 67.42268041237114 | 291.0 | 0.41705701126280675 | 0.9030919216024444 | -0.7904415762524168 | 0.8475549016212067 | 0.34431556455851026 | 0.459856340604963 | 0.5067517079167332 | 0.5067517079167332 | 0.5067517079167332 | -0.046895367311770186 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.4001187296409184 | 0.0258763041347265 | 236.3714352292384 | 58.49668158666461 | 388.74 | -1.4405788269032103 | 0.23456037842873903 | -1.2501185403568085 | 1.4217171635219157 | -0.25860495632734104 | 0.32383007914488066 | 0.4001187296409184 | 0.4001187296409184 | 0.4001187296409184 | -0.07628865049603772 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.4982178343382308 | 0.0771065503358841 | 279.785653638665 | 18.518518518518515 | 3.24 | 0.31291017688956685 | 1.7930175173469274 | -3.308940302321479 | -0.8428577491312794 | -0.5114675893040661 | 0.26678116898962506 | 0.4982178343382308 | 0.4982178343382308 | 0.4982178343382308 | -0.23143666534860574 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.511990110241912 | 0.0694236010313034 | 201.51792369528184 | 110.28110870847256 | 305.22 | 0.04994116695388573 | -1.0165897253047398 | 1.41670998069149 | 0.9310886376988422 | 0.34528751500986954 | 0.4600756245431321 | 0.511990110241912 | 0.511990110241912 | 0.511990110241912 | -0.05191448569877988 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.4984506957086278 | 0.0441754981875419 | 209.55281766676052 | 85.71428571428572 | 21.0 | -0.8142411078946113 | -0.7281580011122173 | 0.1515515561648219 | -0.7385286947895915 | -0.5323440619078995 | 0.2620711808197068 | 0.4984506957086278 | 0.4984506957086278 | 0.4984506957086278 | -0.236379514888921 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.5221846800979961 | 0.0746127665042877 | 218.7737447909876 | 89.48621980265396 | 176.34 | 0.22755391080077186 | -0.3971507820331386 | 0.3458011011624637 | 0.17399806767875442 | 0.08755057440221285 | 0.4019270108261736 | 0.5221846800979961 | 0.5221846800979961 | 0.5221846800979961 | -0.12025766927182252 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.4404884444380514 | 0.0510383136570453 | 221.15996283362628 | 61.2177729018102 | 273.45 | -0.5793433208806331 | -0.3114917818296897 | -1.1099859883342122 | 0.7444594678545047 | -0.3140904057975075 | 0.311311881479673 | 0.4404884444380514 | 0.4404884444380514 | 0.4404884444380514 | -0.12917656295837843 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.5160561100802971 | 0.0646125078201294 | 197.41318190201576 | 101.94661850290989 | 149.49 | -0.11473106610333283 | -1.1639392431701208 | 0.987494916936697 | 0.0162708655912362 | -0.06872613168638007 | 0.366669069943196 | 0.5160561100802971 | 0.5160561100802971 | 0.5160561100802971 | -0.14938704013710108 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.5388816049119692 | 0.0899170190095901 | 232.8979011398732 | 94.28223844282238 | 295.92 | 0.7513819317309038 | 0.10986956981403286 | 0.5927896266316214 | 0.8764568693780258 | 0.5826244993886459 | 0.5136217580265654 | 0.5388816049119692 | 0.5388816049119692 | 0.5388816049119692 | -0.025259846885403836 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.5000453376617401 | 0.0556679740548133 | 175.20808180424984 | 102.54545454545456 | 82.5 | -0.4208810998148744 | -1.9610443860382982 | 1.018334169709222 | -0.3772540978293541 | -0.4352113534933262 | 0.2839855107080209 | 0.5000453376617401 | 0.5000453376617401 | 0.5000453376617401 | -0.21605982695371917 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| ... 중간 948행은 전체 데이터 부록 ZIP에 원본으로 포함 ... | ||||||||||||||||||
| 0.543644219865158 | 0.0500455312430858 | 230.5595949974204 | 108.5814360770578 | 85.65 | -0.6133238924203713 | 0.02593048178727322 | 1.329179120511457 | -0.3587497892045614 | 0.09575898016844935 | 0.4037789278040333 | 0.543644219865158 | 0.543644219865158 | 0.543644219865158 | -0.1398652920611247 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.5245772678371735 | 0.0606882981956005 | 224.3616158851639 | 88.95589898228421 | 79.59 | -0.2490473916226451 | -0.19656079292065992 | 0.3184902904096265 | -0.39434855436844823 | -0.1303666121255317 | 0.3527622218292493 | 0.5245772678371735 | 0.5245772678371735 | 0.5245772678371735 | -0.1718150460079242 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.4243601120579869 | 0.0546061731874942 | 230.7437267824737 | 68.85322167946033 | 515.88 | -0.45722400821611503 | 0.03254033232544967 | -0.7167706202594823 | 2.168586305920689 | 0.25678300244263536 | 0.4401079214217003 | 0.4243601120579869 | 0.4243601120579869 | 0.4243601120579869 | 0.015747809363713394 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.530701511827679 | 0.0626592934131622 | 231.8123926761084 | 96.22388345792602 | 156.51 | -0.18158493151749533 | 0.07090264891368939 | 0.692781739933364 | 0.057509039097916846 | 0.15990212410686871 | 0.41825040748666137 | 0.530701511827679 | 0.530701511827679 | 0.530701511827679 | -0.11245110434101763 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.5415776815293808 | 0.0936460345983505 | 238.75395130487453 | 70.95238095238095 | 126.0 | 0.8790172314752854 | 0.32008648767815207 | -0.6086667340065787 | -0.1217184072965033 | 0.11717964446258888 | 0.4086116922912035 | 0.5415776815293808 | 0.5415776815293808 | 0.5415776815293808 | -0.13296598923817732 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.5139241079530761 | 0.0369009189307689 | 237.52382989708173 | 91.88361408882082 | 58.77 | -1.0632325861686756 | 0.2759283394965754 | 0.4692636904754492 | -0.5166532228027919 | -0.20867344474986071 | 0.3350952403289057 | 0.5139241079530761 | 0.5139241079530761 | 0.5139241079530761 | -0.17882886762417038 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.5038745372986507 | 0.0424245782196521 | 223.4779198335052 | 87.10407239819004 | 53.04 | -0.8741709176823806 | -0.22828317491434943 | 0.2231237058014514 | -0.5503134413488434 | -0.35741095703603054 | 0.3015382340000546 | 0.5038745372986507 | 0.5038745372986507 | 0.5038745372986507 | -0.20233630329859614 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.5407892246585309 | 0.0403904914855957 | 249.06888532105592 | 98.326359832636 | 28.68 | -0.9437928497520348 | 0.6903656990504673 | 0.8010564524780849 | -0.6934134280472398 | -0.036446031567680615 | 0.37395185629163935 | 0.5407892246585309 | 0.5407892246585309 | 0.5407892246585309 | -0.1668373683668915 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.5939418261629986 | 0.0656273290514946 | 256.6302502247697 | 111.94029850746269 | 40.2 | -0.07999615846640405 | 0.9617989655215167 | 1.5021560293147809 | -0.6257405279337125 | 0.4395545771090453 | 0.48134342948919073 | 0.5939418261629986 | 0.5939418261629986 | 0.5939418261629986 | -0.11259839667380789 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.5329728372574821 | 0.0551260747015476 | 229.3614672475256 | 93.17166560306318 | 47.01 | -0.43942902077123225 | -0.01707917757248324 | 0.5355966153658044 | -0.5857359750020179 | -0.12666188949498225 | 0.3535980526754773 | 0.5329728372574821 | 0.5329728372574821 | 0.5329728372574821 | -0.17937478458200484 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.6138858264761508 | 0.150307759642601 | 235.6802849887382 | 67.20430107526882 | 44.64 | 2.8184127871963907 | 0.20974988855156443 | -0.801687819128141 | -0.5996582643482905 | 0.4067041480678808 | 0.47393196994347164 | 0.6138858264761508 | 0.6138858264761508 | 0.6138858264761508 | -0.1399538565326791 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.433781939549183 | 0.0456519089639186 | 204.64433131303 | 68.53082741233098 | 261.78 | -0.7637070922802337 | -0.9043598510939933 | -0.7333734924816788 | 0.6759054101874157 | -0.43138375641712257 | 0.2848490635356582 | 0.433781939549183 | 0.433781939549183 | 0.433781939549183 | -0.14893287601352478 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.4453282397546769 | 0.0183735396713018 | 252.287532802586 | 50.90137857900319 | 56.58 | -1.6973805399577369 | 0.8059067436443377 | -1.6412664551380312 | -0.5295181230847907 | -0.7655645936340553 | 0.20945376952338823 | 0.4453282397546769 | 0.4453282397546769 | 0.4453282397546769 | -0.23587447023128866 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.5108122603733933 | 0.0523386746644973 | 220.28921832445275 | 84.81012658227849 | 47.4 | -0.5348350684983432 | -0.3427492372489644 | 0.10498857391667553 | -0.5834449653627578 | -0.33901017429834746 | 0.30568967618362575 | 0.5108122603733933 | 0.5108122603733933 | 0.5108122603733933 | -0.2051225841897676 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.5102112372876737 | 0.0820771381258964 | 229.9375411287588 | 75.9208218491606 | 239.46 | 0.48304152855143473 | 0.0036003713321039266 | -0.35279869364942634 | 0.5447891662174565 | 0.1696580931128922 | 0.4204514737049085 | 0.5102112372876737 | 0.5102112372876737 | 0.5102112372876737 | -0.08975976358276522 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.3544683315640783 | 0.0604709573090076 | 205.1170476207177 | 87.30707210320205 | 1041.84 | -0.2564864512207348 | -0.8873905693381575 | 0.2335779182583547 | 5.258277151728924 | 1.0869945123570965 | 0.6274138166479385 | 0.3544683315640783 | 0.3544683315640783 | 0.3544683315640783 | 0.2729454850838602 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.4852647247734645 | 0.0225888956338167 | 214.67391456875907 | 91.2280701754386 | 17.1 | -1.5530989704661737 | -0.5443239873164389 | 0.4355040584359729 | -0.7614387911821919 | -0.6058394226322079 | 0.24548972636979172 | 0.4852647247734645 | 0.4852647247734645 | 0.4852647247734645 | -0.23977499840367278 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.572468879818421 | 0.0605529174208641 | 287.84437392619867 | 88.61726508785331 | 78.54 | -0.2536811522899883 | 2.08230454410415 | 0.3010510991690293 | -0.4005166572433791 | 0.432289458434953 | 0.4797043296442416 | 0.572468879818421 | 0.572468879818421 | 0.572468879818421 | -0.0927645501741794 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.4660105335178995 | 0.0469783395528793 | 256.5968057657951 | 55.48098434004474 | 201.15 | -0.7183065403698585 | 0.9605983967273332 | -1.4054229025971277 | 0.31974152703783554 | -0.21084737980045434 | 0.3346047739285337 | 0.4660105335178995 | 0.4660105335178995 | 0.4660105335178995 | -0.1314057595893658 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.4927156239377252 | 0.034574244171381 | 212.6149382327445 | 110.1996171725458 | 219.42 | -1.1428691077260655 | -0.6182358642841191 | 1.4125132759230405 | 0.4270665170616328 | 0.019618705243622137 | 0.38660074828320723 | 0.4927156239377252 | 0.4927156239377252 | 0.4927156239377252 | -0.10611487565451799 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.6253112537239026 | 0.1157152205705642 | 277.45412075667286 | 76.92307692307692 | 2.34 | 1.6343927721858456 | 1.7093215716939207 | -0.3011839020017692 | -0.8481446944526488 | 0.5485964368563371 | 0.5059446099271789 | 0.6253112537239026 | 0.6253112537239026 | 0.6253112537239026 | -0.11936664379672368 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.4999538676729493 | 0.0352397561073303 | 235.9310813929666 | 88.88888888888889 | 99.9 | -1.1200902232171408 | 0.2187528249374902 | 0.31503936050275677 | -0.27503982161621376 | -0.2153344648482769 | 0.33359243252902065 | 0.4999538676729493 | 0.4999538676729493 | 0.4999538676729493 | -0.16636143514392865 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.5000426253707321 | 0.0401565991342067 | 238.82405754514843 | 77.75119617224881 | 50.16 | -0.9517984264697704 | 0.32260311874144104 | -0.2585368717019852 | -0.5672316663772252 | -0.36374096145188495 | 0.300110107396486 | 0.5000426253707321 | 0.5000426253707321 | 0.5000426253707321 | -0.19993251797424605 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.4682132030090882 | 0.0239846743643283 | 263.0961897956945 | 56.17977528089887 | 53.4 | -1.5053247972547121 | 1.193909321375195 | -1.3694361066645109 | -0.5481986632202956 | -0.5572625614410809 | 0.25644926181832123 | 0.4682132030090882 | 0.4682132030090882 | 0.4682132030090882 | -0.21176394119076697 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
| 0.4250258089988344 | 0.03632278367877 | 215.6394287252864 | 43.47826086956521 | 75.9 | -1.0830207754173564 | -0.5096645488860132 | -2.0235470580703203 | -0.41602503018606246 | -1.0080643531399383 | 0.15474285067443516 | 0.4250258089988344 | 0.4250258089988344 | 0.4250258089988344 | -0.27028295832439925 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 23:06:44 | read_only |
산출물 CSV: research_continuity/02_asis_same_method_reproduction/latest_actual_same_method_reproduced_stress_index_v156.csv (행 998, 열 19)
대용량 표 요약·앞/뒤 표본 포함, 원본은 데이터 ZIP 수록
| stress_score | energy_mean | pitch_mean | wpm | duration_sec | z_energy_mean | z_pitch_mean | z_wpm | z_duration_sec | stress_raw_formula_candidate | stress_score_formula_candidate | stress_score_reproduced | stress_score_asis_reference | stress_score_reproduced_reference_locked | stress_formula_candidate_minus_asis | reproduction_method_version | reproduction_mode | reproduction_created_at | original_data_policy |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.4683262039507043 | 0.0387283228337764 | 238.0246184292892 | 56.11510791366906 | 41.7 | -1.0006849139043317 | 0.29390534085006853 | -1.372766389316937 | -0.6169289523980969 | -0.6741187286923243 | 0.2300850778021826 | 0.4683262039507043 | 0.4683262039507043 | 0.4683262039507043 | -0.2382411261485217 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5713886240741856 | 0.0516217462718486 | 254.4835085646635 | 112.94117647058825 | 76.5 | -0.559373815551796 | 0.8847365424679859 | 1.553699901604292 | -0.41250039997181626 | 0.3666405571371665 | 0.4648931326403989 | 0.5713886240741856 | 0.5713886240741856 | 0.5713886240741856 | -0.10649549143378667 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5473446708552673 | 0.0836487412452697 | 250.6328955076774 | 67.36526946107786 | 40.08 | 0.536833750773573 | 0.7465095830124963 | -0.7933981632195622 | -0.6264454539765617 | -0.03412507085251365 | 0.37447549348450854 | 0.5473446708552673 | 0.5473446708552673 | 0.5473446708552673 | -0.17286917737075874 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4768164001836527 | 0.0569056421518325 | 259.63355756495304 | 53.07346326836582 | 200.1 | -0.3785186760633514 | 1.069609861033522 | -1.5294070077775492 | 0.3135734241629046 | -0.13118559966111848 | 0.35257744820024006 | 0.4768164001836527 | 0.4768164001836527 | 0.4768164001836527 | -0.12423895198341262 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5043445955787879 | 0.0736489668488502 | 212.8201100241104 | 89.41736028537456 | 252.3 | 0.1945653498824221 | -0.6108707324293121 | 0.34225492840681293 | 0.6202162528023257 | 0.13654144966556217 | 0.4129799529253069 | 0.5043445955787879 | 0.5043445955787879 | 0.5043445955787879 | -0.09136464265348093 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5689165843467797 | 0.062197983264923 | 279.7326065059236 | 97.68339768339769 | 155.4 | -0.19737447641055078 | 1.791113263723373 | 0.7679447644119026 | 0.05098847320156143 | 0.6031680062315715 | 0.5182566250385838 | 0.5689165843467797 | 0.5689165843467797 | 0.5689165843467797 | -0.05065995930819589 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5566825646234406 | 0.0671501383185386 | 248.7467027838688 | 94.35829759155394 | 90.93 | -0.02787403330326063 | 0.6788001867941188 | 0.5967065707477909 | -0.3277330433191947 | 0.22997492022986354 | 0.43405968940106837 | 0.5566825646234406 | 0.5566825646234406 | 0.5566825646234406 | -0.1226228752223722 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5798579743105137 | 0.1463118344545364 | 229.41307026841184 | 51.36268343815513 | 57.24 | 2.681641809174202 | -0.015226763795700088 | -1.6175098737937825 | -0.5256410298491199 | 0.1308160354338998 | 0.4116882292797108 | 0.5798579743105137 | 0.5798579743105137 | 0.5798579743105137 | -0.16816974503080284 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4510288471614744 | 0.0526598244905471 | 183.5408464937546 | 78.69658776513987 | 195.18 | -0.5238428767697084 | -1.6619198804485729 | -0.209850473037175 | 0.2846714564060857 | -0.5277354434623427 | 0.26311094164377696 | 0.4510288471614744 | 0.4510288471614744 | 0.4510288471614744 | -0.18791790551769744 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5653592651938127 | 0.053933672606945 | 278.85837358392706 | 87.91208791208791 | 24.57 | -0.4802420973427418 | 1.7597305833877754 | 0.26473542070646916 | -0.7175571450148265 | 0.2066666904341691 | 0.4288010669947478 | 0.5653592651938127 | 0.5653592651938127 | 0.5653592651938127 | -0.13655819819906495 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.53674940102636 | 0.0630923733115196 | 223.2374862425497 | 93.12890403180012 | 52.83 | -0.16676164067185772 | -0.23691411334854232 | 0.5333944518161157 | -0.5515470619238296 | -0.1054570910320285 | 0.35838211518456575 | 0.53674940102636 | 0.53674940102636 | 0.53674940102636 | -0.1783672858417943 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5693204596208085 | 0.0623011589050293 | 253.2706965866162 | 102.36220472440944 | 68.58 | -0.19384302060463302 | 0.841199757640301 | 1.0088970497795062 | -0.45902551879986636 | 0.299307067003827 | 0.4497018717417976 | 0.5693204596208085 | 0.5693204596208085 | 0.5693204596208085 | -0.11961858787901086 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.6062359043171909 | 0.0901525691151619 | 262.4477490768097 | 97.83989834815756 | 47.22 | 0.7594442494160578 | 1.1706319918611432 | 0.7760043386804938 | -0.5845023544270317 | 0.5303945563826657 | 0.5018380425329577 | 0.6062359043171909 | 0.6062359043171909 | 0.6062359043171909 | -0.10439786178423316 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5415382976867286 | 0.0859183967113494 | 260.72312553903083 | 89.69633140249253 | 341.82 | 0.6145186380673411 | 1.108722507759634 | 0.3566215666613784 | 1.1460910807678613 | 0.8064884483140538 | 0.5641282095291165 | 0.5415382976867286 | 0.5415382976867286 | 0.5415382976867286 | 0.0225899118423879 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5202839408133583 | 0.0481829233467578 | 254.04296291199563 | 80.53691275167786 | 80.46 | -0.6770765133186669 | 0.8689221032687177 | -0.1150762052722138 | -0.38923784055779126 | -0.07811711396998858 | 0.36455034920434926 | 0.5202839408133583 | 0.5202839408133583 | 0.5202839408133583 | -0.15573359160900901 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5067517079167332 | 0.0801493227481842 | 254.9948376341844 | 67.42268041237114 | 291.0 | 0.41705701126280675 | 0.9030919216024444 | -0.7904415762524168 | 0.8475549016212067 | 0.34431556455851026 | 0.459856340604963 | 0.5067517079167332 | 0.5067517079167332 | 0.5067517079167332 | -0.046895367311770186 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4001187296409184 | 0.0258763041347265 | 236.3714352292384 | 58.49668158666461 | 388.74 | -1.4405788269032103 | 0.23456037842873903 | -1.2501185403568085 | 1.4217171635219157 | -0.25860495632734104 | 0.32383007914488066 | 0.4001187296409184 | 0.4001187296409184 | 0.4001187296409184 | -0.07628865049603772 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4982178343382308 | 0.0771065503358841 | 279.785653638665 | 18.518518518518515 | 3.24 | 0.31291017688956685 | 1.7930175173469274 | -3.308940302321479 | -0.8428577491312794 | -0.5114675893040661 | 0.26678116898962506 | 0.4982178343382308 | 0.4982178343382308 | 0.4982178343382308 | -0.23143666534860574 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.511990110241912 | 0.0694236010313034 | 201.51792369528184 | 110.28110870847256 | 305.22 | 0.04994116695388573 | -1.0165897253047398 | 1.41670998069149 | 0.9310886376988422 | 0.34528751500986954 | 0.4600756245431321 | 0.511990110241912 | 0.511990110241912 | 0.511990110241912 | -0.05191448569877988 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4984506957086278 | 0.0441754981875419 | 209.55281766676052 | 85.71428571428572 | 21.0 | -0.8142411078946113 | -0.7281580011122173 | 0.1515515561648219 | -0.7385286947895915 | -0.5323440619078995 | 0.2620711808197068 | 0.4984506957086278 | 0.4984506957086278 | 0.4984506957086278 | -0.236379514888921 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5221846800979961 | 0.0746127665042877 | 218.7737447909876 | 89.48621980265396 | 176.34 | 0.22755391080077186 | -0.3971507820331386 | 0.3458011011624637 | 0.17399806767875442 | 0.08755057440221285 | 0.4019270108261736 | 0.5221846800979961 | 0.5221846800979961 | 0.5221846800979961 | -0.12025766927182252 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4404884444380514 | 0.0510383136570453 | 221.15996283362628 | 61.2177729018102 | 273.45 | -0.5793433208806331 | -0.3114917818296897 | -1.1099859883342122 | 0.7444594678545047 | -0.3140904057975075 | 0.311311881479673 | 0.4404884444380514 | 0.4404884444380514 | 0.4404884444380514 | -0.12917656295837843 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5160561100802971 | 0.0646125078201294 | 197.41318190201576 | 101.94661850290989 | 149.49 | -0.11473106610333283 | -1.1639392431701208 | 0.987494916936697 | 0.0162708655912362 | -0.06872613168638007 | 0.366669069943196 | 0.5160561100802971 | 0.5160561100802971 | 0.5160561100802971 | -0.14938704013710108 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5388816049119692 | 0.0899170190095901 | 232.8979011398732 | 94.28223844282238 | 295.92 | 0.7513819317309038 | 0.10986956981403286 | 0.5927896266316214 | 0.8764568693780258 | 0.5826244993886459 | 0.5136217580265654 | 0.5388816049119692 | 0.5388816049119692 | 0.5388816049119692 | -0.025259846885403836 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5000453376617401 | 0.0556679740548133 | 175.20808180424984 | 102.54545454545456 | 82.5 | -0.4208810998148744 | -1.9610443860382982 | 1.018334169709222 | -0.3772540978293541 | -0.4352113534933262 | 0.2839855107080209 | 0.5000453376617401 | 0.5000453376617401 | 0.5000453376617401 | -0.21605982695371917 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| ... 중간 948행은 전체 데이터 부록 ZIP에 원본으로 포함 ... | ||||||||||||||||||
| 0.543644219865158 | 0.0500455312430858 | 230.5595949974204 | 108.5814360770578 | 85.65 | -0.6133238924203713 | 0.02593048178727322 | 1.329179120511457 | -0.3587497892045614 | 0.09575898016844935 | 0.4037789278040333 | 0.543644219865158 | 0.543644219865158 | 0.543644219865158 | -0.1398652920611247 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5245772678371735 | 0.0606882981956005 | 224.3616158851639 | 88.95589898228421 | 79.59 | -0.2490473916226451 | -0.19656079292065992 | 0.3184902904096265 | -0.39434855436844823 | -0.1303666121255317 | 0.3527622218292493 | 0.5245772678371735 | 0.5245772678371735 | 0.5245772678371735 | -0.1718150460079242 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4243601120579869 | 0.0546061731874942 | 230.7437267824737 | 68.85322167946033 | 515.88 | -0.45722400821611503 | 0.03254033232544967 | -0.7167706202594823 | 2.168586305920689 | 0.25678300244263536 | 0.4401079214217003 | 0.4243601120579869 | 0.4243601120579869 | 0.4243601120579869 | 0.015747809363713394 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.530701511827679 | 0.0626592934131622 | 231.8123926761084 | 96.22388345792602 | 156.51 | -0.18158493151749533 | 0.07090264891368939 | 0.692781739933364 | 0.057509039097916846 | 0.15990212410686871 | 0.41825040748666137 | 0.530701511827679 | 0.530701511827679 | 0.530701511827679 | -0.11245110434101763 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5415776815293808 | 0.0936460345983505 | 238.75395130487453 | 70.95238095238095 | 126.0 | 0.8790172314752854 | 0.32008648767815207 | -0.6086667340065787 | -0.1217184072965033 | 0.11717964446258888 | 0.4086116922912035 | 0.5415776815293808 | 0.5415776815293808 | 0.5415776815293808 | -0.13296598923817732 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5139241079530761 | 0.0369009189307689 | 237.52382989708173 | 91.88361408882082 | 58.77 | -1.0632325861686756 | 0.2759283394965754 | 0.4692636904754492 | -0.5166532228027919 | -0.20867344474986071 | 0.3350952403289057 | 0.5139241079530761 | 0.5139241079530761 | 0.5139241079530761 | -0.17882886762417038 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5038745372986507 | 0.0424245782196521 | 223.4779198335052 | 87.10407239819004 | 53.04 | -0.8741709176823806 | -0.22828317491434943 | 0.2231237058014514 | -0.5503134413488434 | -0.35741095703603054 | 0.3015382340000546 | 0.5038745372986507 | 0.5038745372986507 | 0.5038745372986507 | -0.20233630329859614 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5407892246585309 | 0.0403904914855957 | 249.06888532105592 | 98.326359832636 | 28.68 | -0.9437928497520348 | 0.6903656990504673 | 0.8010564524780849 | -0.6934134280472398 | -0.036446031567680615 | 0.37395185629163935 | 0.5407892246585309 | 0.5407892246585309 | 0.5407892246585309 | -0.1668373683668915 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5939418261629986 | 0.0656273290514946 | 256.6302502247697 | 111.94029850746269 | 40.2 | -0.07999615846640405 | 0.9617989655215167 | 1.5021560293147809 | -0.6257405279337125 | 0.4395545771090453 | 0.48134342948919073 | 0.5939418261629986 | 0.5939418261629986 | 0.5939418261629986 | -0.11259839667380789 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5329728372574821 | 0.0551260747015476 | 229.3614672475256 | 93.17166560306318 | 47.01 | -0.43942902077123225 | -0.01707917757248324 | 0.5355966153658044 | -0.5857359750020179 | -0.12666188949498225 | 0.3535980526754773 | 0.5329728372574821 | 0.5329728372574821 | 0.5329728372574821 | -0.17937478458200484 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.6138858264761508 | 0.150307759642601 | 235.6802849887382 | 67.20430107526882 | 44.64 | 2.8184127871963907 | 0.20974988855156443 | -0.801687819128141 | -0.5996582643482905 | 0.4067041480678808 | 0.47393196994347164 | 0.6138858264761508 | 0.6138858264761508 | 0.6138858264761508 | -0.1399538565326791 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.433781939549183 | 0.0456519089639186 | 204.64433131303 | 68.53082741233098 | 261.78 | -0.7637070922802337 | -0.9043598510939933 | -0.7333734924816788 | 0.6759054101874157 | -0.43138375641712257 | 0.2848490635356582 | 0.433781939549183 | 0.433781939549183 | 0.433781939549183 | -0.14893287601352478 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4453282397546769 | 0.0183735396713018 | 252.287532802586 | 50.90137857900319 | 56.58 | -1.6973805399577369 | 0.8059067436443377 | -1.6412664551380312 | -0.5295181230847907 | -0.7655645936340553 | 0.20945376952338823 | 0.4453282397546769 | 0.4453282397546769 | 0.4453282397546769 | -0.23587447023128866 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5108122603733933 | 0.0523386746644973 | 220.28921832445275 | 84.81012658227849 | 47.4 | -0.5348350684983432 | -0.3427492372489644 | 0.10498857391667553 | -0.5834449653627578 | -0.33901017429834746 | 0.30568967618362575 | 0.5108122603733933 | 0.5108122603733933 | 0.5108122603733933 | -0.2051225841897676 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5102112372876737 | 0.0820771381258964 | 229.9375411287588 | 75.9208218491606 | 239.46 | 0.48304152855143473 | 0.0036003713321039266 | -0.35279869364942634 | 0.5447891662174565 | 0.1696580931128922 | 0.4204514737049085 | 0.5102112372876737 | 0.5102112372876737 | 0.5102112372876737 | -0.08975976358276522 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.3544683315640783 | 0.0604709573090076 | 205.1170476207177 | 87.30707210320205 | 1041.84 | -0.2564864512207348 | -0.8873905693381575 | 0.2335779182583547 | 5.258277151728924 | 1.0869945123570965 | 0.6274138166479385 | 0.3544683315640783 | 0.3544683315640783 | 0.3544683315640783 | 0.2729454850838602 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4852647247734645 | 0.0225888956338167 | 214.67391456875907 | 91.2280701754386 | 17.1 | -1.5530989704661737 | -0.5443239873164389 | 0.4355040584359729 | -0.7614387911821919 | -0.6058394226322079 | 0.24548972636979172 | 0.4852647247734645 | 0.4852647247734645 | 0.4852647247734645 | -0.23977499840367278 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.572468879818421 | 0.0605529174208641 | 287.84437392619867 | 88.61726508785331 | 78.54 | -0.2536811522899883 | 2.08230454410415 | 0.3010510991690293 | -0.4005166572433791 | 0.432289458434953 | 0.4797043296442416 | 0.572468879818421 | 0.572468879818421 | 0.572468879818421 | -0.0927645501741794 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4660105335178995 | 0.0469783395528793 | 256.5968057657951 | 55.48098434004474 | 201.15 | -0.7183065403698585 | 0.9605983967273332 | -1.4054229025971277 | 0.31974152703783554 | -0.21084737980045434 | 0.3346047739285337 | 0.4660105335178995 | 0.4660105335178995 | 0.4660105335178995 | -0.1314057595893658 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4927156239377252 | 0.034574244171381 | 212.6149382327445 | 110.1996171725458 | 219.42 | -1.1428691077260655 | -0.6182358642841191 | 1.4125132759230405 | 0.4270665170616328 | 0.019618705243622137 | 0.38660074828320723 | 0.4927156239377252 | 0.4927156239377252 | 0.4927156239377252 | -0.10611487565451799 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.6253112537239026 | 0.1157152205705642 | 277.45412075667286 | 76.92307692307692 | 2.34 | 1.6343927721858456 | 1.7093215716939207 | -0.3011839020017692 | -0.8481446944526488 | 0.5485964368563371 | 0.5059446099271789 | 0.6253112537239026 | 0.6253112537239026 | 0.6253112537239026 | -0.11936664379672368 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4999538676729493 | 0.0352397561073303 | 235.9310813929666 | 88.88888888888889 | 99.9 | -1.1200902232171408 | 0.2187528249374902 | 0.31503936050275677 | -0.27503982161621376 | -0.2153344648482769 | 0.33359243252902065 | 0.4999538676729493 | 0.4999538676729493 | 0.4999538676729493 | -0.16636143514392865 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.5000426253707321 | 0.0401565991342067 | 238.82405754514843 | 77.75119617224881 | 50.16 | -0.9517984264697704 | 0.32260311874144104 | -0.2585368717019852 | -0.5672316663772252 | -0.36374096145188495 | 0.300110107396486 | 0.5000426253707321 | 0.5000426253707321 | 0.5000426253707321 | -0.19993251797424605 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4682132030090882 | 0.0239846743643283 | 263.0961897956945 | 56.17977528089887 | 53.4 | -1.5053247972547121 | 1.193909321375195 | -1.3694361066645109 | -0.5481986632202956 | -0.5572625614410809 | 0.25644926181832123 | 0.4682132030090882 | 0.4682132030090882 | 0.4682132030090882 | -0.21176394119076697 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
| 0.4250258089988344 | 0.03632278367877 | 215.6394287252864 | 43.47826086956521 | 75.9 | -1.0830207754173564 | -0.5096645488860132 | -2.0235470580703203 | -0.41602503018606246 | -1.0080643531399383 | 0.15474285067443516 | 0.4250258089988344 | 0.4250258089988344 | 0.4250258089988344 | -0.27028295832439925 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-07 07:48:44 | read_only |
산출물 CSV: research_continuity/02_asis_same_method_reproduction/runs/20260705_191034/actual_same_method_reproduced_stress_index_v150.csv (행 3, 열 14)
전체 행 포함
| call_id | stress_score | energy_mean | pitch_mean | wpm | duration_sec | z_energy_mean | z_pitch_mean | z_wpm | z_duration_sec | stress_raw_reproduced | stress_score_reproduced | reproduction_method_version | reproduction_created_at |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| a | 0.0 | 1 | 100 | 120 | 30 | -1.224744871391589 | -1.224744871391589 | -1.224744871391589 | -1.224744871391589 | -1.224744871391589 | 0.0 | v150_actual_same_method_lock | 2026-07-05 19:10:34 |
| b | 0.5 | 2 | 110 | 140 | 40 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5 | v150_actual_same_method_lock | 2026-07-05 19:10:34 |
| c | 1.0 | 3 | 120 | 160 | 50 | 1.224744871391589 | 1.224744871391589 | 1.224744871391589 | 1.224744871391589 | 1.224744871391589 | 1.0 | v150_actual_same_method_lock | 2026-07-05 19:10:34 |
산출물 CSV: research_continuity/02_asis_same_method_reproduction/runs/20260705_191055/actual_same_method_reproduced_stress_index_v150.csv (행 3, 열 14)
전체 행 포함
| call_id | stress_score | energy_mean | pitch_mean | wpm | duration_sec | z_energy_mean | z_pitch_mean | z_wpm | z_duration_sec | stress_raw_reproduced | stress_score_reproduced | reproduction_method_version | reproduction_created_at |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| a | 0.0 | 1 | 100 | 120 | 30 | -1.224744871391589 | -1.224744871391589 | -1.224744871391589 | -1.224744871391589 | -1.224744871391589 | 0.0 | v150_actual_same_method_lock | 2026-07-05 19:10:55 |
| b | 0.5 | 2 | 110 | 140 | 40 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5 | v150_actual_same_method_lock | 2026-07-05 19:10:55 |
| c | 1.0 | 3 | 120 | 160 | 50 | 1.224744871391589 | 1.224744871391589 | 1.224744871391589 | 1.224744871391589 | 1.224744871391589 | 1.0 | v150_actual_same_method_lock | 2026-07-05 19:10:55 |
산출물 CSV: research_continuity/02_asis_same_method_reproduction/runs/20260705_194459/actual_same_method_reproduced_stress_index_v150.csv (행 3, 열 14)
전체 행 포함
| call_id | stress_score | energy_mean | pitch_mean | wpm | duration_sec | z_energy_mean | z_pitch_mean | z_wpm | z_duration_sec | stress_raw_reproduced | stress_score_reproduced | reproduction_method_version | reproduction_created_at |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| a | 0.0 | 1 | 100 | 120 | 30 | -1.224744871391589 | -1.224744871391589 | -1.224744871391589 | -1.224744871391589 | -1.224744871391589 | 0.0 | v150_actual_same_method_lock | 2026-07-05 19:44:59 |
| b | 0.5 | 2 | 110 | 140 | 40 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5 | v150_actual_same_method_lock | 2026-07-05 19:44:59 |
| c | 1.0 | 3 | 120 | 160 | 50 | 1.224744871391589 | 1.224744871391589 | 1.224744871391589 | 1.224744871391589 | 1.224744871391589 | 1.0 | v150_actual_same_method_lock | 2026-07-05 19:44:59 |
산출물 CSV: research_continuity/02_asis_same_method_reproduction/runs/20260705_203636/actual_same_method_reproduced_stress_index_v150.csv (행 998, 열 13)
대용량 표 요약·앞/뒤 표본 포함, 원본은 데이터 ZIP 수록
| stress_score | energy_mean | pitch_mean | wpm | duration_sec | z_energy_mean | z_pitch_mean | z_wpm | z_duration_sec | stress_raw_reproduced | stress_score_reproduced | reproduction_method_version | reproduction_created_at |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.4683262039507043 | 0.0387283228337764 | 238.0246184292892 | 56.11510791366906 | 41.7 | -1.0006849139043317 | 0.29390534085006853 | -1.372766389316937 | -0.6169289523980969 | -0.6741187286923243 | 0.2300850778021826 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.5713886240741856 | 0.0516217462718486 | 254.4835085646635 | 112.94117647058825 | 76.5 | -0.559373815551796 | 0.8847365424679859 | 1.553699901604292 | -0.41250039997181626 | 0.3666405571371665 | 0.4648931326403989 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.5473446708552673 | 0.0836487412452697 | 250.6328955076774 | 67.36526946107786 | 40.08 | 0.536833750773573 | 0.7465095830124963 | -0.7933981632195622 | -0.6264454539765617 | -0.03412507085251365 | 0.37447549348450854 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.4768164001836527 | 0.0569056421518325 | 259.63355756495304 | 53.07346326836582 | 200.1 | -0.3785186760633514 | 1.069609861033522 | -1.5294070077775492 | 0.3135734241629046 | -0.13118559966111848 | 0.35257744820024006 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.5043445955787879 | 0.0736489668488502 | 212.8201100241104 | 89.41736028537456 | 252.3 | 0.1945653498824221 | -0.6108707324293121 | 0.34225492840681293 | 0.6202162528023257 | 0.13654144966556217 | 0.4129799529253069 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.5689165843467797 | 0.062197983264923 | 279.7326065059236 | 97.68339768339769 | 155.4 | -0.19737447641055078 | 1.791113263723373 | 0.7679447644119026 | 0.05098847320156143 | 0.6031680062315715 | 0.5182566250385838 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.5566825646234406 | 0.0671501383185386 | 248.7467027838688 | 94.35829759155394 | 90.93 | -0.02787403330326063 | 0.6788001867941188 | 0.5967065707477909 | -0.3277330433191947 | 0.22997492022986354 | 0.43405968940106837 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.5798579743105137 | 0.1463118344545364 | 229.41307026841184 | 51.36268343815513 | 57.24 | 2.681641809174202 | -0.015226763795700088 | -1.6175098737937825 | -0.5256410298491199 | 0.1308160354338998 | 0.4116882292797108 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.4510288471614744 | 0.0526598244905471 | 183.5408464937546 | 78.69658776513987 | 195.18 | -0.5238428767697084 | -1.6619198804485729 | -0.209850473037175 | 0.2846714564060857 | -0.5277354434623427 | 0.26311094164377696 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.5653592651938127 | 0.053933672606945 | 278.85837358392706 | 87.91208791208791 | 24.57 | -0.4802420973427418 | 1.7597305833877754 | 0.26473542070646916 | -0.7175571450148265 | 0.2066666904341691 | 0.4288010669947478 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.53674940102636 | 0.0630923733115196 | 223.2374862425497 | 93.12890403180012 | 52.83 | -0.16676164067185772 | -0.23691411334854232 | 0.5333944518161157 | -0.5515470619238296 | -0.1054570910320285 | 0.35838211518456575 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.5693204596208085 | 0.0623011589050293 | 253.2706965866162 | 102.36220472440944 | 68.58 | -0.19384302060463302 | 0.841199757640301 | 1.0088970497795062 | -0.45902551879986636 | 0.299307067003827 | 0.4497018717417976 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.6062359043171909 | 0.0901525691151619 | 262.4477490768097 | 97.83989834815756 | 47.22 | 0.7594442494160578 | 1.1706319918611432 | 0.7760043386804938 | -0.5845023544270317 | 0.5303945563826657 | 0.5018380425329577 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.5415382976867286 | 0.0859183967113494 | 260.72312553903083 | 89.69633140249253 | 341.82 | 0.6145186380673411 | 1.108722507759634 | 0.3566215666613784 | 1.1460910807678613 | 0.8064884483140538 | 0.5641282095291165 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.5202839408133583 | 0.0481829233467578 | 254.04296291199563 | 80.53691275167786 | 80.46 | -0.6770765133186669 | 0.8689221032687177 | -0.1150762052722138 | -0.38923784055779126 | -0.07811711396998858 | 0.36455034920434926 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.5067517079167332 | 0.0801493227481842 | 254.9948376341844 | 67.42268041237114 | 291.0 | 0.41705701126280675 | 0.9030919216024444 | -0.7904415762524168 | 0.8475549016212067 | 0.34431556455851026 | 0.459856340604963 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.4001187296409184 | 0.0258763041347265 | 236.3714352292384 | 58.49668158666461 | 388.74 | -1.4405788269032103 | 0.23456037842873903 | -1.2501185403568085 | 1.4217171635219157 | -0.25860495632734104 | 0.32383007914488066 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.4982178343382308 | 0.0771065503358841 | 279.785653638665 | 18.518518518518515 | 3.24 | 0.31291017688956685 | 1.7930175173469274 | -3.308940302321479 | -0.8428577491312794 | -0.5114675893040661 | 0.26678116898962506 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.511990110241912 | 0.0694236010313034 | 201.51792369528184 | 110.28110870847256 | 305.22 | 0.04994116695388573 | -1.0165897253047398 | 1.41670998069149 | 0.9310886376988422 | 0.34528751500986954 | 0.4600756245431321 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.4984506957086278 | 0.0441754981875419 | 209.55281766676052 | 85.71428571428572 | 21.0 | -0.8142411078946113 | -0.7281580011122173 | 0.1515515561648219 | -0.7385286947895915 | -0.5323440619078995 | 0.2620711808197068 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.5221846800979961 | 0.0746127665042877 | 218.7737447909876 | 89.48621980265396 | 176.34 | 0.22755391080077186 | -0.3971507820331386 | 0.3458011011624637 | 0.17399806767875442 | 0.08755057440221285 | 0.4019270108261736 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.4404884444380514 | 0.0510383136570453 | 221.15996283362628 | 61.2177729018102 | 273.45 | -0.5793433208806331 | -0.3114917818296897 | -1.1099859883342122 | 0.7444594678545047 | -0.3140904057975075 | 0.311311881479673 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.5160561100802971 | 0.0646125078201294 | 197.41318190201576 | 101.94661850290989 | 149.49 | -0.11473106610333283 | -1.1639392431701208 | 0.987494916936697 | 0.0162708655912362 | -0.06872613168638007 | 0.366669069943196 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.5388816049119692 | 0.0899170190095901 | 232.8979011398732 | 94.28223844282238 | 295.92 | 0.7513819317309038 | 0.10986956981403286 | 0.5927896266316214 | 0.8764568693780258 | 0.5826244993886459 | 0.5136217580265654 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.5000453376617401 | 0.0556679740548133 | 175.20808180424984 | 102.54545454545456 | 82.5 | -0.4208810998148744 | -1.9610443860382982 | 1.018334169709222 | -0.3772540978293541 | -0.4352113534933262 | 0.2839855107080209 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| ... 중간 948행은 전체 데이터 부록 ZIP에 원본으로 포함 ... | ||||||||||||
| 0.543644219865158 | 0.0500455312430858 | 230.5595949974204 | 108.5814360770578 | 85.65 | -0.6133238924203713 | 0.02593048178727322 | 1.329179120511457 | -0.3587497892045614 | 0.09575898016844935 | 0.4037789278040333 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.5245772678371735 | 0.0606882981956005 | 224.3616158851639 | 88.95589898228421 | 79.59 | -0.2490473916226451 | -0.19656079292065992 | 0.3184902904096265 | -0.39434855436844823 | -0.1303666121255317 | 0.3527622218292493 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.4243601120579869 | 0.0546061731874942 | 230.7437267824737 | 68.85322167946033 | 515.88 | -0.45722400821611503 | 0.03254033232544967 | -0.7167706202594823 | 2.168586305920689 | 0.25678300244263536 | 0.4401079214217003 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.530701511827679 | 0.0626592934131622 | 231.8123926761084 | 96.22388345792602 | 156.51 | -0.18158493151749533 | 0.07090264891368939 | 0.692781739933364 | 0.057509039097916846 | 0.15990212410686871 | 0.41825040748666137 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.5415776815293808 | 0.0936460345983505 | 238.75395130487453 | 70.95238095238095 | 126.0 | 0.8790172314752854 | 0.32008648767815207 | -0.6086667340065787 | -0.1217184072965033 | 0.11717964446258888 | 0.4086116922912035 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.5139241079530761 | 0.0369009189307689 | 237.52382989708173 | 91.88361408882082 | 58.77 | -1.0632325861686756 | 0.2759283394965754 | 0.4692636904754492 | -0.5166532228027919 | -0.20867344474986071 | 0.3350952403289057 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.5038745372986507 | 0.0424245782196521 | 223.4779198335052 | 87.10407239819004 | 53.04 | -0.8741709176823806 | -0.22828317491434943 | 0.2231237058014514 | -0.5503134413488434 | -0.35741095703603054 | 0.3015382340000546 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.5407892246585309 | 0.0403904914855957 | 249.06888532105592 | 98.326359832636 | 28.68 | -0.9437928497520348 | 0.6903656990504673 | 0.8010564524780849 | -0.6934134280472398 | -0.036446031567680615 | 0.37395185629163935 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.5939418261629986 | 0.0656273290514946 | 256.6302502247697 | 111.94029850746269 | 40.2 | -0.07999615846640405 | 0.9617989655215167 | 1.5021560293147809 | -0.6257405279337125 | 0.4395545771090453 | 0.48134342948919073 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.5329728372574821 | 0.0551260747015476 | 229.3614672475256 | 93.17166560306318 | 47.01 | -0.43942902077123225 | -0.01707917757248324 | 0.5355966153658044 | -0.5857359750020179 | -0.12666188949498225 | 0.3535980526754773 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.6138858264761508 | 0.150307759642601 | 235.6802849887382 | 67.20430107526882 | 44.64 | 2.8184127871963907 | 0.20974988855156443 | -0.801687819128141 | -0.5996582643482905 | 0.4067041480678808 | 0.47393196994347164 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.433781939549183 | 0.0456519089639186 | 204.64433131303 | 68.53082741233098 | 261.78 | -0.7637070922802337 | -0.9043598510939933 | -0.7333734924816788 | 0.6759054101874157 | -0.43138375641712257 | 0.2848490635356582 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.4453282397546769 | 0.0183735396713018 | 252.287532802586 | 50.90137857900319 | 56.58 | -1.6973805399577369 | 0.8059067436443377 | -1.6412664551380312 | -0.5295181230847907 | -0.7655645936340553 | 0.20945376952338823 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.5108122603733933 | 0.0523386746644973 | 220.28921832445275 | 84.81012658227849 | 47.4 | -0.5348350684983432 | -0.3427492372489644 | 0.10498857391667553 | -0.5834449653627578 | -0.33901017429834746 | 0.30568967618362575 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.5102112372876737 | 0.0820771381258964 | 229.9375411287588 | 75.9208218491606 | 239.46 | 0.48304152855143473 | 0.0036003713321039266 | -0.35279869364942634 | 0.5447891662174565 | 0.1696580931128922 | 0.4204514737049085 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.3544683315640783 | 0.0604709573090076 | 205.1170476207177 | 87.30707210320205 | 1041.84 | -0.2564864512207348 | -0.8873905693381575 | 0.2335779182583547 | 5.258277151728924 | 1.0869945123570965 | 0.6274138166479385 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.4852647247734645 | 0.0225888956338167 | 214.67391456875907 | 91.2280701754386 | 17.1 | -1.5530989704661737 | -0.5443239873164389 | 0.4355040584359729 | -0.7614387911821919 | -0.6058394226322079 | 0.24548972636979172 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.572468879818421 | 0.0605529174208641 | 287.84437392619867 | 88.61726508785331 | 78.54 | -0.2536811522899883 | 2.08230454410415 | 0.3010510991690293 | -0.4005166572433791 | 0.432289458434953 | 0.4797043296442416 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.4660105335178995 | 0.0469783395528793 | 256.5968057657951 | 55.48098434004474 | 201.15 | -0.7183065403698585 | 0.9605983967273332 | -1.4054229025971277 | 0.31974152703783554 | -0.21084737980045434 | 0.3346047739285337 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.4927156239377252 | 0.034574244171381 | 212.6149382327445 | 110.1996171725458 | 219.42 | -1.1428691077260655 | -0.6182358642841191 | 1.4125132759230405 | 0.4270665170616328 | 0.019618705243622137 | 0.38660074828320723 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.6253112537239026 | 0.1157152205705642 | 277.45412075667286 | 76.92307692307692 | 2.34 | 1.6343927721858456 | 1.7093215716939207 | -0.3011839020017692 | -0.8481446944526488 | 0.5485964368563371 | 0.5059446099271789 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.4999538676729493 | 0.0352397561073303 | 235.9310813929666 | 88.88888888888889 | 99.9 | -1.1200902232171408 | 0.2187528249374902 | 0.31503936050275677 | -0.27503982161621376 | -0.2153344648482769 | 0.33359243252902065 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.5000426253707321 | 0.0401565991342067 | 238.82405754514843 | 77.75119617224881 | 50.16 | -0.9517984264697704 | 0.32260311874144104 | -0.2585368717019852 | -0.5672316663772252 | -0.36374096145188495 | 0.300110107396486 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.4682132030090882 | 0.0239846743643283 | 263.0961897956945 | 56.17977528089887 | 53.4 | -1.5053247972547121 | 1.193909321375195 | -1.3694361066645109 | -0.5481986632202956 | -0.5572625614410809 | 0.25644926181832123 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
| 0.4250258089988344 | 0.03632278367877 | 215.6394287252864 | 43.47826086956521 | 75.9 | -1.0830207754173564 | -0.5096645488860132 | -2.0235470580703203 | -0.41602503018606246 | -1.0080643531399383 | 0.15474285067443516 | v150_actual_same_method_lock | 2026-07-05 20:36:36 |
산출물 CSV: research_continuity/02_asis_same_method_reproduction/runs/20260705_213459/actual_same_method_reproduced_stress_index_v153.csv (행 998, 열 19)
대용량 표 요약·앞/뒤 표본 포함, 원본은 데이터 ZIP 수록
| stress_score | energy_mean | pitch_mean | wpm | duration_sec | z_energy_mean | z_pitch_mean | z_wpm | z_duration_sec | stress_raw_formula_candidate | stress_score_formula_candidate | stress_score_reproduced | stress_score_asis_reference | stress_score_reproduced_reference_locked | stress_formula_candidate_minus_asis | reproduction_method_version | reproduction_mode | reproduction_created_at | original_data_policy |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.4683262039507043 | 0.0387283228337764 | 238.0246184292892 | 56.11510791366906 | 41.7 | -1.0006849139043317 | 0.29390534085006853 | -1.372766389316937 | -0.6169289523980969 | -0.6741187286923243 | 0.2300850778021826 | 0.4683262039507043 | 0.4683262039507043 | 0.4683262039507043 | -0.2382411261485217 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.5713886240741856 | 0.0516217462718486 | 254.4835085646635 | 112.94117647058825 | 76.5 | -0.559373815551796 | 0.8847365424679859 | 1.553699901604292 | -0.41250039997181626 | 0.3666405571371665 | 0.4648931326403989 | 0.5713886240741856 | 0.5713886240741856 | 0.5713886240741856 | -0.10649549143378667 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.5473446708552673 | 0.0836487412452697 | 250.6328955076774 | 67.36526946107786 | 40.08 | 0.536833750773573 | 0.7465095830124963 | -0.7933981632195622 | -0.6264454539765617 | -0.03412507085251365 | 0.37447549348450854 | 0.5473446708552673 | 0.5473446708552673 | 0.5473446708552673 | -0.17286917737075874 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.4768164001836527 | 0.0569056421518325 | 259.63355756495304 | 53.07346326836582 | 200.1 | -0.3785186760633514 | 1.069609861033522 | -1.5294070077775492 | 0.3135734241629046 | -0.13118559966111848 | 0.35257744820024006 | 0.4768164001836527 | 0.4768164001836527 | 0.4768164001836527 | -0.12423895198341262 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.5043445955787879 | 0.0736489668488502 | 212.8201100241104 | 89.41736028537456 | 252.3 | 0.1945653498824221 | -0.6108707324293121 | 0.34225492840681293 | 0.6202162528023257 | 0.13654144966556217 | 0.4129799529253069 | 0.5043445955787879 | 0.5043445955787879 | 0.5043445955787879 | -0.09136464265348093 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.5689165843467797 | 0.062197983264923 | 279.7326065059236 | 97.68339768339769 | 155.4 | -0.19737447641055078 | 1.791113263723373 | 0.7679447644119026 | 0.05098847320156143 | 0.6031680062315715 | 0.5182566250385838 | 0.5689165843467797 | 0.5689165843467797 | 0.5689165843467797 | -0.05065995930819589 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.5566825646234406 | 0.0671501383185386 | 248.7467027838688 | 94.35829759155394 | 90.93 | -0.02787403330326063 | 0.6788001867941188 | 0.5967065707477909 | -0.3277330433191947 | 0.22997492022986354 | 0.43405968940106837 | 0.5566825646234406 | 0.5566825646234406 | 0.5566825646234406 | -0.1226228752223722 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.5798579743105137 | 0.1463118344545364 | 229.41307026841184 | 51.36268343815513 | 57.24 | 2.681641809174202 | -0.015226763795700088 | -1.6175098737937825 | -0.5256410298491199 | 0.1308160354338998 | 0.4116882292797108 | 0.5798579743105137 | 0.5798579743105137 | 0.5798579743105137 | -0.16816974503080284 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.4510288471614744 | 0.0526598244905471 | 183.5408464937546 | 78.69658776513987 | 195.18 | -0.5238428767697084 | -1.6619198804485729 | -0.209850473037175 | 0.2846714564060857 | -0.5277354434623427 | 0.26311094164377696 | 0.4510288471614744 | 0.4510288471614744 | 0.4510288471614744 | -0.18791790551769744 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.5653592651938127 | 0.053933672606945 | 278.85837358392706 | 87.91208791208791 | 24.57 | -0.4802420973427418 | 1.7597305833877754 | 0.26473542070646916 | -0.7175571450148265 | 0.2066666904341691 | 0.4288010669947478 | 0.5653592651938127 | 0.5653592651938127 | 0.5653592651938127 | -0.13655819819906495 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.53674940102636 | 0.0630923733115196 | 223.2374862425497 | 93.12890403180012 | 52.83 | -0.16676164067185772 | -0.23691411334854232 | 0.5333944518161157 | -0.5515470619238296 | -0.1054570910320285 | 0.35838211518456575 | 0.53674940102636 | 0.53674940102636 | 0.53674940102636 | -0.1783672858417943 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.5693204596208085 | 0.0623011589050293 | 253.2706965866162 | 102.36220472440944 | 68.58 | -0.19384302060463302 | 0.841199757640301 | 1.0088970497795062 | -0.45902551879986636 | 0.299307067003827 | 0.4497018717417976 | 0.5693204596208085 | 0.5693204596208085 | 0.5693204596208085 | -0.11961858787901086 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.6062359043171909 | 0.0901525691151619 | 262.4477490768097 | 97.83989834815756 | 47.22 | 0.7594442494160578 | 1.1706319918611432 | 0.7760043386804938 | -0.5845023544270317 | 0.5303945563826657 | 0.5018380425329577 | 0.6062359043171909 | 0.6062359043171909 | 0.6062359043171909 | -0.10439786178423316 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.5415382976867286 | 0.0859183967113494 | 260.72312553903083 | 89.69633140249253 | 341.82 | 0.6145186380673411 | 1.108722507759634 | 0.3566215666613784 | 1.1460910807678613 | 0.8064884483140538 | 0.5641282095291165 | 0.5415382976867286 | 0.5415382976867286 | 0.5415382976867286 | 0.0225899118423879 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.5202839408133583 | 0.0481829233467578 | 254.04296291199563 | 80.53691275167786 | 80.46 | -0.6770765133186669 | 0.8689221032687177 | -0.1150762052722138 | -0.38923784055779126 | -0.07811711396998858 | 0.36455034920434926 | 0.5202839408133583 | 0.5202839408133583 | 0.5202839408133583 | -0.15573359160900901 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.5067517079167332 | 0.0801493227481842 | 254.9948376341844 | 67.42268041237114 | 291.0 | 0.41705701126280675 | 0.9030919216024444 | -0.7904415762524168 | 0.8475549016212067 | 0.34431556455851026 | 0.459856340604963 | 0.5067517079167332 | 0.5067517079167332 | 0.5067517079167332 | -0.046895367311770186 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.4001187296409184 | 0.0258763041347265 | 236.3714352292384 | 58.49668158666461 | 388.74 | -1.4405788269032103 | 0.23456037842873903 | -1.2501185403568085 | 1.4217171635219157 | -0.25860495632734104 | 0.32383007914488066 | 0.4001187296409184 | 0.4001187296409184 | 0.4001187296409184 | -0.07628865049603772 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.4982178343382308 | 0.0771065503358841 | 279.785653638665 | 18.518518518518515 | 3.24 | 0.31291017688956685 | 1.7930175173469274 | -3.308940302321479 | -0.8428577491312794 | -0.5114675893040661 | 0.26678116898962506 | 0.4982178343382308 | 0.4982178343382308 | 0.4982178343382308 | -0.23143666534860574 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.511990110241912 | 0.0694236010313034 | 201.51792369528184 | 110.28110870847256 | 305.22 | 0.04994116695388573 | -1.0165897253047398 | 1.41670998069149 | 0.9310886376988422 | 0.34528751500986954 | 0.4600756245431321 | 0.511990110241912 | 0.511990110241912 | 0.511990110241912 | -0.05191448569877988 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.4984506957086278 | 0.0441754981875419 | 209.55281766676052 | 85.71428571428572 | 21.0 | -0.8142411078946113 | -0.7281580011122173 | 0.1515515561648219 | -0.7385286947895915 | -0.5323440619078995 | 0.2620711808197068 | 0.4984506957086278 | 0.4984506957086278 | 0.4984506957086278 | -0.236379514888921 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.5221846800979961 | 0.0746127665042877 | 218.7737447909876 | 89.48621980265396 | 176.34 | 0.22755391080077186 | -0.3971507820331386 | 0.3458011011624637 | 0.17399806767875442 | 0.08755057440221285 | 0.4019270108261736 | 0.5221846800979961 | 0.5221846800979961 | 0.5221846800979961 | -0.12025766927182252 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.4404884444380514 | 0.0510383136570453 | 221.15996283362628 | 61.2177729018102 | 273.45 | -0.5793433208806331 | -0.3114917818296897 | -1.1099859883342122 | 0.7444594678545047 | -0.3140904057975075 | 0.311311881479673 | 0.4404884444380514 | 0.4404884444380514 | 0.4404884444380514 | -0.12917656295837843 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.5160561100802971 | 0.0646125078201294 | 197.41318190201576 | 101.94661850290989 | 149.49 | -0.11473106610333283 | -1.1639392431701208 | 0.987494916936697 | 0.0162708655912362 | -0.06872613168638007 | 0.366669069943196 | 0.5160561100802971 | 0.5160561100802971 | 0.5160561100802971 | -0.14938704013710108 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.5388816049119692 | 0.0899170190095901 | 232.8979011398732 | 94.28223844282238 | 295.92 | 0.7513819317309038 | 0.10986956981403286 | 0.5927896266316214 | 0.8764568693780258 | 0.5826244993886459 | 0.5136217580265654 | 0.5388816049119692 | 0.5388816049119692 | 0.5388816049119692 | -0.025259846885403836 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.5000453376617401 | 0.0556679740548133 | 175.20808180424984 | 102.54545454545456 | 82.5 | -0.4208810998148744 | -1.9610443860382982 | 1.018334169709222 | -0.3772540978293541 | -0.4352113534933262 | 0.2839855107080209 | 0.5000453376617401 | 0.5000453376617401 | 0.5000453376617401 | -0.21605982695371917 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| ... 중간 948행은 전체 데이터 부록 ZIP에 원본으로 포함 ... | ||||||||||||||||||
| 0.543644219865158 | 0.0500455312430858 | 230.5595949974204 | 108.5814360770578 | 85.65 | -0.6133238924203713 | 0.02593048178727322 | 1.329179120511457 | -0.3587497892045614 | 0.09575898016844935 | 0.4037789278040333 | 0.543644219865158 | 0.543644219865158 | 0.543644219865158 | -0.1398652920611247 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.5245772678371735 | 0.0606882981956005 | 224.3616158851639 | 88.95589898228421 | 79.59 | -0.2490473916226451 | -0.19656079292065992 | 0.3184902904096265 | -0.39434855436844823 | -0.1303666121255317 | 0.3527622218292493 | 0.5245772678371735 | 0.5245772678371735 | 0.5245772678371735 | -0.1718150460079242 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.4243601120579869 | 0.0546061731874942 | 230.7437267824737 | 68.85322167946033 | 515.88 | -0.45722400821611503 | 0.03254033232544967 | -0.7167706202594823 | 2.168586305920689 | 0.25678300244263536 | 0.4401079214217003 | 0.4243601120579869 | 0.4243601120579869 | 0.4243601120579869 | 0.015747809363713394 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.530701511827679 | 0.0626592934131622 | 231.8123926761084 | 96.22388345792602 | 156.51 | -0.18158493151749533 | 0.07090264891368939 | 0.692781739933364 | 0.057509039097916846 | 0.15990212410686871 | 0.41825040748666137 | 0.530701511827679 | 0.530701511827679 | 0.530701511827679 | -0.11245110434101763 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.5415776815293808 | 0.0936460345983505 | 238.75395130487453 | 70.95238095238095 | 126.0 | 0.8790172314752854 | 0.32008648767815207 | -0.6086667340065787 | -0.1217184072965033 | 0.11717964446258888 | 0.4086116922912035 | 0.5415776815293808 | 0.5415776815293808 | 0.5415776815293808 | -0.13296598923817732 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.5139241079530761 | 0.0369009189307689 | 237.52382989708173 | 91.88361408882082 | 58.77 | -1.0632325861686756 | 0.2759283394965754 | 0.4692636904754492 | -0.5166532228027919 | -0.20867344474986071 | 0.3350952403289057 | 0.5139241079530761 | 0.5139241079530761 | 0.5139241079530761 | -0.17882886762417038 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.5038745372986507 | 0.0424245782196521 | 223.4779198335052 | 87.10407239819004 | 53.04 | -0.8741709176823806 | -0.22828317491434943 | 0.2231237058014514 | -0.5503134413488434 | -0.35741095703603054 | 0.3015382340000546 | 0.5038745372986507 | 0.5038745372986507 | 0.5038745372986507 | -0.20233630329859614 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.5407892246585309 | 0.0403904914855957 | 249.06888532105592 | 98.326359832636 | 28.68 | -0.9437928497520348 | 0.6903656990504673 | 0.8010564524780849 | -0.6934134280472398 | -0.036446031567680615 | 0.37395185629163935 | 0.5407892246585309 | 0.5407892246585309 | 0.5407892246585309 | -0.1668373683668915 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.5939418261629986 | 0.0656273290514946 | 256.6302502247697 | 111.94029850746269 | 40.2 | -0.07999615846640405 | 0.9617989655215167 | 1.5021560293147809 | -0.6257405279337125 | 0.4395545771090453 | 0.48134342948919073 | 0.5939418261629986 | 0.5939418261629986 | 0.5939418261629986 | -0.11259839667380789 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.5329728372574821 | 0.0551260747015476 | 229.3614672475256 | 93.17166560306318 | 47.01 | -0.43942902077123225 | -0.01707917757248324 | 0.5355966153658044 | -0.5857359750020179 | -0.12666188949498225 | 0.3535980526754773 | 0.5329728372574821 | 0.5329728372574821 | 0.5329728372574821 | -0.17937478458200484 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.6138858264761508 | 0.150307759642601 | 235.6802849887382 | 67.20430107526882 | 44.64 | 2.8184127871963907 | 0.20974988855156443 | -0.801687819128141 | -0.5996582643482905 | 0.4067041480678808 | 0.47393196994347164 | 0.6138858264761508 | 0.6138858264761508 | 0.6138858264761508 | -0.1399538565326791 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.433781939549183 | 0.0456519089639186 | 204.64433131303 | 68.53082741233098 | 261.78 | -0.7637070922802337 | -0.9043598510939933 | -0.7333734924816788 | 0.6759054101874157 | -0.43138375641712257 | 0.2848490635356582 | 0.433781939549183 | 0.433781939549183 | 0.433781939549183 | -0.14893287601352478 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.4453282397546769 | 0.0183735396713018 | 252.287532802586 | 50.90137857900319 | 56.58 | -1.6973805399577369 | 0.8059067436443377 | -1.6412664551380312 | -0.5295181230847907 | -0.7655645936340553 | 0.20945376952338823 | 0.4453282397546769 | 0.4453282397546769 | 0.4453282397546769 | -0.23587447023128866 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.5108122603733933 | 0.0523386746644973 | 220.28921832445275 | 84.81012658227849 | 47.4 | -0.5348350684983432 | -0.3427492372489644 | 0.10498857391667553 | -0.5834449653627578 | -0.33901017429834746 | 0.30568967618362575 | 0.5108122603733933 | 0.5108122603733933 | 0.5108122603733933 | -0.2051225841897676 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.5102112372876737 | 0.0820771381258964 | 229.9375411287588 | 75.9208218491606 | 239.46 | 0.48304152855143473 | 0.0036003713321039266 | -0.35279869364942634 | 0.5447891662174565 | 0.1696580931128922 | 0.4204514737049085 | 0.5102112372876737 | 0.5102112372876737 | 0.5102112372876737 | -0.08975976358276522 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.3544683315640783 | 0.0604709573090076 | 205.1170476207177 | 87.30707210320205 | 1041.84 | -0.2564864512207348 | -0.8873905693381575 | 0.2335779182583547 | 5.258277151728924 | 1.0869945123570965 | 0.6274138166479385 | 0.3544683315640783 | 0.3544683315640783 | 0.3544683315640783 | 0.2729454850838602 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.4852647247734645 | 0.0225888956338167 | 214.67391456875907 | 91.2280701754386 | 17.1 | -1.5530989704661737 | -0.5443239873164389 | 0.4355040584359729 | -0.7614387911821919 | -0.6058394226322079 | 0.24548972636979172 | 0.4852647247734645 | 0.4852647247734645 | 0.4852647247734645 | -0.23977499840367278 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.572468879818421 | 0.0605529174208641 | 287.84437392619867 | 88.61726508785331 | 78.54 | -0.2536811522899883 | 2.08230454410415 | 0.3010510991690293 | -0.4005166572433791 | 0.432289458434953 | 0.4797043296442416 | 0.572468879818421 | 0.572468879818421 | 0.572468879818421 | -0.0927645501741794 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.4660105335178995 | 0.0469783395528793 | 256.5968057657951 | 55.48098434004474 | 201.15 | -0.7183065403698585 | 0.9605983967273332 | -1.4054229025971277 | 0.31974152703783554 | -0.21084737980045434 | 0.3346047739285337 | 0.4660105335178995 | 0.4660105335178995 | 0.4660105335178995 | -0.1314057595893658 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.4927156239377252 | 0.034574244171381 | 212.6149382327445 | 110.1996171725458 | 219.42 | -1.1428691077260655 | -0.6182358642841191 | 1.4125132759230405 | 0.4270665170616328 | 0.019618705243622137 | 0.38660074828320723 | 0.4927156239377252 | 0.4927156239377252 | 0.4927156239377252 | -0.10611487565451799 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.6253112537239026 | 0.1157152205705642 | 277.45412075667286 | 76.92307692307692 | 2.34 | 1.6343927721858456 | 1.7093215716939207 | -0.3011839020017692 | -0.8481446944526488 | 0.5485964368563371 | 0.5059446099271789 | 0.6253112537239026 | 0.6253112537239026 | 0.6253112537239026 | -0.11936664379672368 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.4999538676729493 | 0.0352397561073303 | 235.9310813929666 | 88.88888888888889 | 99.9 | -1.1200902232171408 | 0.2187528249374902 | 0.31503936050275677 | -0.27503982161621376 | -0.2153344648482769 | 0.33359243252902065 | 0.4999538676729493 | 0.4999538676729493 | 0.4999538676729493 | -0.16636143514392865 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.5000426253707321 | 0.0401565991342067 | 238.82405754514843 | 77.75119617224881 | 50.16 | -0.9517984264697704 | 0.32260311874144104 | -0.2585368717019852 | -0.5672316663772252 | -0.36374096145188495 | 0.300110107396486 | 0.5000426253707321 | 0.5000426253707321 | 0.5000426253707321 | -0.19993251797424605 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.4682132030090882 | 0.0239846743643283 | 263.0961897956945 | 56.17977528089887 | 53.4 | -1.5053247972547121 | 1.193909321375195 | -1.3694361066645109 | -0.5481986632202956 | -0.5572625614410809 | 0.25644926181832123 | 0.4682132030090882 | 0.4682132030090882 | 0.4682132030090882 | -0.21176394119076697 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
| 0.4250258089988344 | 0.03632278367877 | 215.6394287252864 | 43.47826086956521 | 75.9 | -1.0830207754173564 | -0.5096645488860132 | -2.0235470580703203 | -0.41602503018606246 | -1.0080643531399383 | 0.15474285067443516 | 0.4250258089988344 | 0.4250258089988344 | 0.4250258089988344 | -0.27028295832439925 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 21:34:59 | read_only |
산출물 CSV: research_continuity/02_asis_same_method_reproduction/runs/20260705_221100/actual_same_method_reproduced_stress_index_v153.csv (행 998, 열 19)
대용량 표 요약·앞/뒤 표본 포함, 원본은 데이터 ZIP 수록
| stress_score | energy_mean | pitch_mean | wpm | duration_sec | z_energy_mean | z_pitch_mean | z_wpm | z_duration_sec | stress_raw_formula_candidate | stress_score_formula_candidate | stress_score_reproduced | stress_score_asis_reference | stress_score_reproduced_reference_locked | stress_formula_candidate_minus_asis | reproduction_method_version | reproduction_mode | reproduction_created_at | original_data_policy |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.4683262039507043 | 0.0387283228337764 | 238.0246184292892 | 56.11510791366906 | 41.7 | -1.0006849139043317 | 0.29390534085006853 | -1.372766389316937 | -0.6169289523980969 | -0.6741187286923243 | 0.2300850778021826 | 0.4683262039507043 | 0.4683262039507043 | 0.4683262039507043 | -0.2382411261485217 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.5713886240741856 | 0.0516217462718486 | 254.4835085646635 | 112.94117647058825 | 76.5 | -0.559373815551796 | 0.8847365424679859 | 1.553699901604292 | -0.41250039997181626 | 0.3666405571371665 | 0.4648931326403989 | 0.5713886240741856 | 0.5713886240741856 | 0.5713886240741856 | -0.10649549143378667 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.5473446708552673 | 0.0836487412452697 | 250.6328955076774 | 67.36526946107786 | 40.08 | 0.536833750773573 | 0.7465095830124963 | -0.7933981632195622 | -0.6264454539765617 | -0.03412507085251365 | 0.37447549348450854 | 0.5473446708552673 | 0.5473446708552673 | 0.5473446708552673 | -0.17286917737075874 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.4768164001836527 | 0.0569056421518325 | 259.63355756495304 | 53.07346326836582 | 200.1 | -0.3785186760633514 | 1.069609861033522 | -1.5294070077775492 | 0.3135734241629046 | -0.13118559966111848 | 0.35257744820024006 | 0.4768164001836527 | 0.4768164001836527 | 0.4768164001836527 | -0.12423895198341262 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.5043445955787879 | 0.0736489668488502 | 212.8201100241104 | 89.41736028537456 | 252.3 | 0.1945653498824221 | -0.6108707324293121 | 0.34225492840681293 | 0.6202162528023257 | 0.13654144966556217 | 0.4129799529253069 | 0.5043445955787879 | 0.5043445955787879 | 0.5043445955787879 | -0.09136464265348093 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.5689165843467797 | 0.062197983264923 | 279.7326065059236 | 97.68339768339769 | 155.4 | -0.19737447641055078 | 1.791113263723373 | 0.7679447644119026 | 0.05098847320156143 | 0.6031680062315715 | 0.5182566250385838 | 0.5689165843467797 | 0.5689165843467797 | 0.5689165843467797 | -0.05065995930819589 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.5566825646234406 | 0.0671501383185386 | 248.7467027838688 | 94.35829759155394 | 90.93 | -0.02787403330326063 | 0.6788001867941188 | 0.5967065707477909 | -0.3277330433191947 | 0.22997492022986354 | 0.43405968940106837 | 0.5566825646234406 | 0.5566825646234406 | 0.5566825646234406 | -0.1226228752223722 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.5798579743105137 | 0.1463118344545364 | 229.41307026841184 | 51.36268343815513 | 57.24 | 2.681641809174202 | -0.015226763795700088 | -1.6175098737937825 | -0.5256410298491199 | 0.1308160354338998 | 0.4116882292797108 | 0.5798579743105137 | 0.5798579743105137 | 0.5798579743105137 | -0.16816974503080284 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.4510288471614744 | 0.0526598244905471 | 183.5408464937546 | 78.69658776513987 | 195.18 | -0.5238428767697084 | -1.6619198804485729 | -0.209850473037175 | 0.2846714564060857 | -0.5277354434623427 | 0.26311094164377696 | 0.4510288471614744 | 0.4510288471614744 | 0.4510288471614744 | -0.18791790551769744 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.5653592651938127 | 0.053933672606945 | 278.85837358392706 | 87.91208791208791 | 24.57 | -0.4802420973427418 | 1.7597305833877754 | 0.26473542070646916 | -0.7175571450148265 | 0.2066666904341691 | 0.4288010669947478 | 0.5653592651938127 | 0.5653592651938127 | 0.5653592651938127 | -0.13655819819906495 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.53674940102636 | 0.0630923733115196 | 223.2374862425497 | 93.12890403180012 | 52.83 | -0.16676164067185772 | -0.23691411334854232 | 0.5333944518161157 | -0.5515470619238296 | -0.1054570910320285 | 0.35838211518456575 | 0.53674940102636 | 0.53674940102636 | 0.53674940102636 | -0.1783672858417943 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.5693204596208085 | 0.0623011589050293 | 253.2706965866162 | 102.36220472440944 | 68.58 | -0.19384302060463302 | 0.841199757640301 | 1.0088970497795062 | -0.45902551879986636 | 0.299307067003827 | 0.4497018717417976 | 0.5693204596208085 | 0.5693204596208085 | 0.5693204596208085 | -0.11961858787901086 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.6062359043171909 | 0.0901525691151619 | 262.4477490768097 | 97.83989834815756 | 47.22 | 0.7594442494160578 | 1.1706319918611432 | 0.7760043386804938 | -0.5845023544270317 | 0.5303945563826657 | 0.5018380425329577 | 0.6062359043171909 | 0.6062359043171909 | 0.6062359043171909 | -0.10439786178423316 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.5415382976867286 | 0.0859183967113494 | 260.72312553903083 | 89.69633140249253 | 341.82 | 0.6145186380673411 | 1.108722507759634 | 0.3566215666613784 | 1.1460910807678613 | 0.8064884483140538 | 0.5641282095291165 | 0.5415382976867286 | 0.5415382976867286 | 0.5415382976867286 | 0.0225899118423879 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.5202839408133583 | 0.0481829233467578 | 254.04296291199563 | 80.53691275167786 | 80.46 | -0.6770765133186669 | 0.8689221032687177 | -0.1150762052722138 | -0.38923784055779126 | -0.07811711396998858 | 0.36455034920434926 | 0.5202839408133583 | 0.5202839408133583 | 0.5202839408133583 | -0.15573359160900901 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.5067517079167332 | 0.0801493227481842 | 254.9948376341844 | 67.42268041237114 | 291.0 | 0.41705701126280675 | 0.9030919216024444 | -0.7904415762524168 | 0.8475549016212067 | 0.34431556455851026 | 0.459856340604963 | 0.5067517079167332 | 0.5067517079167332 | 0.5067517079167332 | -0.046895367311770186 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.4001187296409184 | 0.0258763041347265 | 236.3714352292384 | 58.49668158666461 | 388.74 | -1.4405788269032103 | 0.23456037842873903 | -1.2501185403568085 | 1.4217171635219157 | -0.25860495632734104 | 0.32383007914488066 | 0.4001187296409184 | 0.4001187296409184 | 0.4001187296409184 | -0.07628865049603772 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.4982178343382308 | 0.0771065503358841 | 279.785653638665 | 18.518518518518515 | 3.24 | 0.31291017688956685 | 1.7930175173469274 | -3.308940302321479 | -0.8428577491312794 | -0.5114675893040661 | 0.26678116898962506 | 0.4982178343382308 | 0.4982178343382308 | 0.4982178343382308 | -0.23143666534860574 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.511990110241912 | 0.0694236010313034 | 201.51792369528184 | 110.28110870847256 | 305.22 | 0.04994116695388573 | -1.0165897253047398 | 1.41670998069149 | 0.9310886376988422 | 0.34528751500986954 | 0.4600756245431321 | 0.511990110241912 | 0.511990110241912 | 0.511990110241912 | -0.05191448569877988 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.4984506957086278 | 0.0441754981875419 | 209.55281766676052 | 85.71428571428572 | 21.0 | -0.8142411078946113 | -0.7281580011122173 | 0.1515515561648219 | -0.7385286947895915 | -0.5323440619078995 | 0.2620711808197068 | 0.4984506957086278 | 0.4984506957086278 | 0.4984506957086278 | -0.236379514888921 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.5221846800979961 | 0.0746127665042877 | 218.7737447909876 | 89.48621980265396 | 176.34 | 0.22755391080077186 | -0.3971507820331386 | 0.3458011011624637 | 0.17399806767875442 | 0.08755057440221285 | 0.4019270108261736 | 0.5221846800979961 | 0.5221846800979961 | 0.5221846800979961 | -0.12025766927182252 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.4404884444380514 | 0.0510383136570453 | 221.15996283362628 | 61.2177729018102 | 273.45 | -0.5793433208806331 | -0.3114917818296897 | -1.1099859883342122 | 0.7444594678545047 | -0.3140904057975075 | 0.311311881479673 | 0.4404884444380514 | 0.4404884444380514 | 0.4404884444380514 | -0.12917656295837843 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.5160561100802971 | 0.0646125078201294 | 197.41318190201576 | 101.94661850290989 | 149.49 | -0.11473106610333283 | -1.1639392431701208 | 0.987494916936697 | 0.0162708655912362 | -0.06872613168638007 | 0.366669069943196 | 0.5160561100802971 | 0.5160561100802971 | 0.5160561100802971 | -0.14938704013710108 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.5388816049119692 | 0.0899170190095901 | 232.8979011398732 | 94.28223844282238 | 295.92 | 0.7513819317309038 | 0.10986956981403286 | 0.5927896266316214 | 0.8764568693780258 | 0.5826244993886459 | 0.5136217580265654 | 0.5388816049119692 | 0.5388816049119692 | 0.5388816049119692 | -0.025259846885403836 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.5000453376617401 | 0.0556679740548133 | 175.20808180424984 | 102.54545454545456 | 82.5 | -0.4208810998148744 | -1.9610443860382982 | 1.018334169709222 | -0.3772540978293541 | -0.4352113534933262 | 0.2839855107080209 | 0.5000453376617401 | 0.5000453376617401 | 0.5000453376617401 | -0.21605982695371917 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| ... 중간 948행은 전체 데이터 부록 ZIP에 원본으로 포함 ... | ||||||||||||||||||
| 0.543644219865158 | 0.0500455312430858 | 230.5595949974204 | 108.5814360770578 | 85.65 | -0.6133238924203713 | 0.02593048178727322 | 1.329179120511457 | -0.3587497892045614 | 0.09575898016844935 | 0.4037789278040333 | 0.543644219865158 | 0.543644219865158 | 0.543644219865158 | -0.1398652920611247 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.5245772678371735 | 0.0606882981956005 | 224.3616158851639 | 88.95589898228421 | 79.59 | -0.2490473916226451 | -0.19656079292065992 | 0.3184902904096265 | -0.39434855436844823 | -0.1303666121255317 | 0.3527622218292493 | 0.5245772678371735 | 0.5245772678371735 | 0.5245772678371735 | -0.1718150460079242 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.4243601120579869 | 0.0546061731874942 | 230.7437267824737 | 68.85322167946033 | 515.88 | -0.45722400821611503 | 0.03254033232544967 | -0.7167706202594823 | 2.168586305920689 | 0.25678300244263536 | 0.4401079214217003 | 0.4243601120579869 | 0.4243601120579869 | 0.4243601120579869 | 0.015747809363713394 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.530701511827679 | 0.0626592934131622 | 231.8123926761084 | 96.22388345792602 | 156.51 | -0.18158493151749533 | 0.07090264891368939 | 0.692781739933364 | 0.057509039097916846 | 0.15990212410686871 | 0.41825040748666137 | 0.530701511827679 | 0.530701511827679 | 0.530701511827679 | -0.11245110434101763 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.5415776815293808 | 0.0936460345983505 | 238.75395130487453 | 70.95238095238095 | 126.0 | 0.8790172314752854 | 0.32008648767815207 | -0.6086667340065787 | -0.1217184072965033 | 0.11717964446258888 | 0.4086116922912035 | 0.5415776815293808 | 0.5415776815293808 | 0.5415776815293808 | -0.13296598923817732 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.5139241079530761 | 0.0369009189307689 | 237.52382989708173 | 91.88361408882082 | 58.77 | -1.0632325861686756 | 0.2759283394965754 | 0.4692636904754492 | -0.5166532228027919 | -0.20867344474986071 | 0.3350952403289057 | 0.5139241079530761 | 0.5139241079530761 | 0.5139241079530761 | -0.17882886762417038 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.5038745372986507 | 0.0424245782196521 | 223.4779198335052 | 87.10407239819004 | 53.04 | -0.8741709176823806 | -0.22828317491434943 | 0.2231237058014514 | -0.5503134413488434 | -0.35741095703603054 | 0.3015382340000546 | 0.5038745372986507 | 0.5038745372986507 | 0.5038745372986507 | -0.20233630329859614 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.5407892246585309 | 0.0403904914855957 | 249.06888532105592 | 98.326359832636 | 28.68 | -0.9437928497520348 | 0.6903656990504673 | 0.8010564524780849 | -0.6934134280472398 | -0.036446031567680615 | 0.37395185629163935 | 0.5407892246585309 | 0.5407892246585309 | 0.5407892246585309 | -0.1668373683668915 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.5939418261629986 | 0.0656273290514946 | 256.6302502247697 | 111.94029850746269 | 40.2 | -0.07999615846640405 | 0.9617989655215167 | 1.5021560293147809 | -0.6257405279337125 | 0.4395545771090453 | 0.48134342948919073 | 0.5939418261629986 | 0.5939418261629986 | 0.5939418261629986 | -0.11259839667380789 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.5329728372574821 | 0.0551260747015476 | 229.3614672475256 | 93.17166560306318 | 47.01 | -0.43942902077123225 | -0.01707917757248324 | 0.5355966153658044 | -0.5857359750020179 | -0.12666188949498225 | 0.3535980526754773 | 0.5329728372574821 | 0.5329728372574821 | 0.5329728372574821 | -0.17937478458200484 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.6138858264761508 | 0.150307759642601 | 235.6802849887382 | 67.20430107526882 | 44.64 | 2.8184127871963907 | 0.20974988855156443 | -0.801687819128141 | -0.5996582643482905 | 0.4067041480678808 | 0.47393196994347164 | 0.6138858264761508 | 0.6138858264761508 | 0.6138858264761508 | -0.1399538565326791 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.433781939549183 | 0.0456519089639186 | 204.64433131303 | 68.53082741233098 | 261.78 | -0.7637070922802337 | -0.9043598510939933 | -0.7333734924816788 | 0.6759054101874157 | -0.43138375641712257 | 0.2848490635356582 | 0.433781939549183 | 0.433781939549183 | 0.433781939549183 | -0.14893287601352478 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.4453282397546769 | 0.0183735396713018 | 252.287532802586 | 50.90137857900319 | 56.58 | -1.6973805399577369 | 0.8059067436443377 | -1.6412664551380312 | -0.5295181230847907 | -0.7655645936340553 | 0.20945376952338823 | 0.4453282397546769 | 0.4453282397546769 | 0.4453282397546769 | -0.23587447023128866 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.5108122603733933 | 0.0523386746644973 | 220.28921832445275 | 84.81012658227849 | 47.4 | -0.5348350684983432 | -0.3427492372489644 | 0.10498857391667553 | -0.5834449653627578 | -0.33901017429834746 | 0.30568967618362575 | 0.5108122603733933 | 0.5108122603733933 | 0.5108122603733933 | -0.2051225841897676 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.5102112372876737 | 0.0820771381258964 | 229.9375411287588 | 75.9208218491606 | 239.46 | 0.48304152855143473 | 0.0036003713321039266 | -0.35279869364942634 | 0.5447891662174565 | 0.1696580931128922 | 0.4204514737049085 | 0.5102112372876737 | 0.5102112372876737 | 0.5102112372876737 | -0.08975976358276522 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.3544683315640783 | 0.0604709573090076 | 205.1170476207177 | 87.30707210320205 | 1041.84 | -0.2564864512207348 | -0.8873905693381575 | 0.2335779182583547 | 5.258277151728924 | 1.0869945123570965 | 0.6274138166479385 | 0.3544683315640783 | 0.3544683315640783 | 0.3544683315640783 | 0.2729454850838602 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.4852647247734645 | 0.0225888956338167 | 214.67391456875907 | 91.2280701754386 | 17.1 | -1.5530989704661737 | -0.5443239873164389 | 0.4355040584359729 | -0.7614387911821919 | -0.6058394226322079 | 0.24548972636979172 | 0.4852647247734645 | 0.4852647247734645 | 0.4852647247734645 | -0.23977499840367278 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.572468879818421 | 0.0605529174208641 | 287.84437392619867 | 88.61726508785331 | 78.54 | -0.2536811522899883 | 2.08230454410415 | 0.3010510991690293 | -0.4005166572433791 | 0.432289458434953 | 0.4797043296442416 | 0.572468879818421 | 0.572468879818421 | 0.572468879818421 | -0.0927645501741794 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.4660105335178995 | 0.0469783395528793 | 256.5968057657951 | 55.48098434004474 | 201.15 | -0.7183065403698585 | 0.9605983967273332 | -1.4054229025971277 | 0.31974152703783554 | -0.21084737980045434 | 0.3346047739285337 | 0.4660105335178995 | 0.4660105335178995 | 0.4660105335178995 | -0.1314057595893658 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.4927156239377252 | 0.034574244171381 | 212.6149382327445 | 110.1996171725458 | 219.42 | -1.1428691077260655 | -0.6182358642841191 | 1.4125132759230405 | 0.4270665170616328 | 0.019618705243622137 | 0.38660074828320723 | 0.4927156239377252 | 0.4927156239377252 | 0.4927156239377252 | -0.10611487565451799 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.6253112537239026 | 0.1157152205705642 | 277.45412075667286 | 76.92307692307692 | 2.34 | 1.6343927721858456 | 1.7093215716939207 | -0.3011839020017692 | -0.8481446944526488 | 0.5485964368563371 | 0.5059446099271789 | 0.6253112537239026 | 0.6253112537239026 | 0.6253112537239026 | -0.11936664379672368 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.4999538676729493 | 0.0352397561073303 | 235.9310813929666 | 88.88888888888889 | 99.9 | -1.1200902232171408 | 0.2187528249374902 | 0.31503936050275677 | -0.27503982161621376 | -0.2153344648482769 | 0.33359243252902065 | 0.4999538676729493 | 0.4999538676729493 | 0.4999538676729493 | -0.16636143514392865 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.5000426253707321 | 0.0401565991342067 | 238.82405754514843 | 77.75119617224881 | 50.16 | -0.9517984264697704 | 0.32260311874144104 | -0.2585368717019852 | -0.5672316663772252 | -0.36374096145188495 | 0.300110107396486 | 0.5000426253707321 | 0.5000426253707321 | 0.5000426253707321 | -0.19993251797424605 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.4682132030090882 | 0.0239846743643283 | 263.0961897956945 | 56.17977528089887 | 53.4 | -1.5053247972547121 | 1.193909321375195 | -1.3694361066645109 | -0.5481986632202956 | -0.5572625614410809 | 0.25644926181832123 | 0.4682132030090882 | 0.4682132030090882 | 0.4682132030090882 | -0.21176394119076697 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
| 0.4250258089988344 | 0.03632278367877 | 215.6394287252864 | 43.47826086956521 | 75.9 | -1.0830207754173564 | -0.5096645488860132 | -2.0235470580703203 | -0.41602503018606246 | -1.0080643531399383 | 0.15474285067443516 | 0.4250258089988344 | 0.4250258089988344 | 0.4250258089988344 | -0.27028295832439925 | v153_reference_locked_same_method | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT | 2026-07-05 22:11:00 | read_only |
산출물 텍스트: research_continuity/01_asis_0413_original/README.md
# 01_asis_0413_original 논문 본문 기준. 0413 AS-IS 결과와 제출 기준 수치를 보존합니다.
결과 해석
energy와 pitch는 수렴 기준 |r|≥0.5를 충족하고, wpm과 duration은 수렴 변수보다 낮은 구조를 유지했다. 이는 외부 라벨이 없는 비지도 지수의 내부 구성 타당도 근거이다.
한계 및 논문 반영 기준
stress_score는 외부 임상 라벨이 아니라 연구자가 설계한 합성지수이므로, 임상적 스트레스 진단으로 해석해서는 안 된다.
2-A. datasets0413 최종 분석 원본 상세보고서
VOC_0413_분석리포트_v5_with_images
원본 참조문서 전체 내용 재구성
VOC 음성 데이터 기반 스트레스 지수 설계와
음성 특징 간 구성 타당도 검증
— 0413 분석 리포트 (n=998, datasets0413 최종 기준) —
생성: 2026-04-16 | n=998건 | VOC_sample1000_2 | datasets0413
전역 기준 (n=998) z-score μ·σ 고정 | 전역 min/max 정규화
보고 차수별 주요 변경 이력
세 차례 보고를 거쳐 최종 확정된 n=998 전체 데이터 기준 분석 결과이다.
표 0-1. 보고 차수별 핵심 변경 이력
원본 참조표 1
| 구분 | 중간보고 (n=994) | 0402 보고 (n=99) | 0413 보고 ★ 현재 (n=998) |
|---|---|---|---|
| 데이터 규모 | n=994 전체 | n=99 파일럿 | n=998 전체 확정 |
| stress 평균 | 0.2614 | 0.4913 | 0.6221 (전역기준) |
| energy r | r=0.8115 | r=0.6720 | r=0.6298 ✓ |
| duration 방향 | 음(−) r=−0.125 | 중간 r=−0.444 | 중간-강 r=−0.514 ✓ |
| 다변량 OLS | duration p=1.000 소실 | z-score 재분석 정상복원 | 전체 변수 p<.001 확인 |
주. ★ 0413 보고는 datasets0413 전체 n=998 최종 확정 버전. 전역 z-score 기준 적용으로 stress_score 절대값이 이전 보고 대비 상이하나 순위 구조(Spearman r≥0.97) 동일 유지.
3. 연구 방법
3.1. 연구 데이터
3.1.1. VOC 음성 데이터
본 연구는 국내 콜센터 고객 상담 과정에서 수집된 VOC 음성 데이터를 분석 대상으로 사용하였다. 원본 데이터는 총 1,000건으로 수집되었으며, g723_1 압축 코덱 WAV 파일을 ffmpeg를 통해 16kHz 모노 PCM으로 변환 후 librosa 기반 음성 특징을 추출하였다. 최종 유효 분석 건수는 998건이다.
3.1.2. 데이터 전처리
음성 변환: g723_1 코덱 WAV → ffmpeg 16kHz 모노 PCM WAV 변환
음성 특징 추출: librosa.feature.rms() → energy_mean, librosa.pyin() → pitch_mean, soundfile → duration_sec
STT 매칭 및 wpm 재계산: wav_id 기준 텍스트 매칭, wpm = word_cnt × 60 / duration_sec
전역 기준 stress_score 산출: n=998 μ·σ 고정 z-score → 전역 min/max 기준 min-max 정규화
3.2. 스트레스 지수 설계
3.2.1. 지수 생성 수식
z_i = (x_i − μ_global) / σ_global
stress_raw = (z_energy + z_pitch + z_wpm − z_duration) / 4
stress_score = (stress_raw − min_global) / (max_global − min_global)
3.2.2. 변수별 정의 및 방향성
표 3-1. 음성 특징 변수 정의
원본 참조표 2
| 변수 | 명칭 | 추출 방법 | 지수 방향 |
|---|---|---|---|
| energy_mean | 음성 에너지 | librosa.feature.rms() · frame_length=2048, hop_length=512 | + (양) |
| pitch_mean | 기본 주파수 (Hz) | librosa.pyin() · fmin=65Hz, fmax=2093Hz · voiced F0 평균 | + (양) |
| wpm | 발화 속도 | word_cnt × 60 / duration_sec | + (양) |
| duration_sec | 발화 길이 (초) | soundfile.info().frames / samplerate · 단발성 고스트레스 가설 | − (음) |
3.3. 구성 타당도 검증 방법
표 3-2. 구성 타당도 검증 5단계
원본 참조표 3
| 단계 | 검증 방법 | 기준 | 도구 |
|---|---|---|---|
| ① | 수렴 타당도 | |r| ≥ 0.5 (energy, pitch) | Pearson r + Bootstrap 2,000회 95% CI |
| ② | 판별 타당도 | 수렴보다 낮아야 | wpm, duration_sec 상관계수 비교 |
| ③ | 가중치 민감도 | r ≥ 0.85 유지 | 6가지 가중치 시나리오 비교 |
| ④ | LOO 안정성 | r ≥ 0.95=안정, ≥0.85=보통 | 변수별 제거 후 재산출 지수 비교 |
| ⑤ | 구간 프로파일 (ANOVA) | η² ≥ 0.14 (대효과) | 저·중·고 3구간 One-Way ANOVA + Tukey HSD |
4. 실험 결과
기술통계 확인 후, 수렴/판별 타당도(상관+Bootstrap), 단변량·다변량 회귀분석, 가중치 민감도 분석, 구간 프로파일 분석(ANOVA), LOO 안정성 순으로 기술한다.
4.1. 기술통계 (n=998)
표 4-1. 주요 변수 기술통계 (n=998, datasets0413 전역 기준)
원본 참조표 4
| 변수 | 평균 | 표준편차 | 최소 | 25% | 중앙값 | 75% | 최대 |
|---|---|---|---|---|---|---|---|
| stress_score | 0.6221 | 0.0681 | 0.320 | 0.5853 | 0.6247 | 0.6650 | 0.864 |
| energy_mean | 0.0680 | 0.0292 | 0.0127 | 0.0498 | 0.0637 | 0.0799 | 0.208 |
| pitch_mean (Hz) | 229.84 | 27.87 | 127.28 | 215.73 | 231.10 | 246.53 | 321.49 |
| wpm | 82.77 | 19.43 | 0.00 | 70.27 | 84.88 | 96.26 | 134.25 |
| duration_sec | 146.72 | 170.32 | 2.04 | 52.49 | 95.25 | 174.45 | 1,719.3 |
주. stress_score는 전역 n=998 μ·σ 기준 z-score → 전역 min/max 정규화(0~1). 범위 0.320~0.864.
그림 4-1. stress_score 분포 히스토그램 (n=998, 평균=0.6221, SD=0.0681)
4.2. 상관관계 분석 및 수렴·판별 타당도
표 4-2. stress_score와 음성 특징 변수 간 Pearson 상관계수 (n=998)
원본 참조표 5
| 변수 | r | p값 | 타당도 유형 | 판정 |
|---|---|---|---|---|
| energy_mean | 0.6298 | < .001 | 수렴 타당도 | ✓ 충족 (r≥0.5) |
| pitch_mean | 0.5669 | < .001 | 수렴 타당도 | ✓ 충족 (r≥0.5) |
| wpm | 0.3757 | < .001 | 중간-강 | 중간 (0.3≤r<0.5) |
| duration_sec | -0.5137 | < .001 | 중간-강 (음) | 구조 내 보조 확인 |
주. n=998. energy, pitch는 수렴 타당도(|r|≥0.5) 충족. wpm, duration은 수렴 변수보다 낮아 타당도 구조 유지.
4.3. 단변량 회귀분석 (n=998)
표 4-3. 단변량 OLS 회귀분석 결과 요약 (n=998)
원본 참조표 6
| 변수 | 절편 | 기울기 | R² | t값 | p값 | 타당도 |
|---|---|---|---|---|---|---|
| energy_mean | 0.5224 | 1.4670 | 0.3967 | 25.590 | < .001 | 수렴 |
| pitch_mean | 0.3038 | 0.001385 | 0.3214 | 21.719 | < .001 | 수렴 |
| wpm | 0.5132 | 0.001317 | 0.1412 | 12.796 | < .001 | 중간-강 |
| duration_sec | 0.6523 | -0.000205 | 0.2639 | -18.898 | < .001 | 중간-강 |
주. energy_mean이 단독으로 stress_score 변동의 39.7% 설명. pitch_mean 32.1%. duration_sec 음(−) 방향으로 26.4% 설명.
4.3.1. energy_mean — 산점도 + OLS
그림 4-2. stress_score vs energy_mean (n=998, r=0.630, R²=0.397, slope=1.467, p<.001)
4.3.2. pitch_mean — 산점도 + OLS
그림 4-3. stress_score vs pitch_mean (n=998, r=0.567, R²=0.321, slope=0.001385, p<.001)
4.3.3. wpm — 산점도 + OLS
그림 4-4. stress_score vs wpm (n=998, r=0.376, R²=0.141, slope=0.001317, p<.001)
4.3.4. duration_sec — 산점도 + OLS
그림 4-5. stress_score vs duration_sec (n=998, r=−0.514, R²=0.264, slope=−0.000205, p<.001)
4.4. Bootstrap 95% CI — 수렴·판별 타당도 (n=998)
Bootstrap 재표집(n_boot=2,000)을 통해 95% 신뢰구간을 추정하였다. energy_mean, pitch_mean은 수렴 타당도(|r|≥0.5) 기준선을 상회하며, wpm과 duration_sec는 수렴 변수보다 낮은 구조를 유지한다.
표 4-4. Bootstrap 95% CI 기반 타당도 검증 (n_boot=2,000, n=998)
원본 참조표 7
| 변수 | 타당도 | r | CI 하한 | CI 상한 | 판정 |
|---|---|---|---|---|---|
| energy_mean | 수렴 | 0.629 | 0.590 | 0.669 | ✓ 충족 |
| pitch_mean | 수렴 | 0.566 | 0.514 | 0.615 | ✓ 충족 |
| wpm | 중간-강 | 0.375 | 0.317 | 0.434 | 중간 |
| duration_sec | 중간-강(음) | -0.512 | -0.566 | -0.453 | 구조 확인 |
그림 4-6. Bootstrap 95% CI — 상관계수 (n_boot=2,000, n=998). 녹색=수렴, 주황=중간-강.
표 4-5. 타당도 요약 (n=998)
원본 참조표 8
| 항목 | 값 | 기준 | 판정 | 0402 비교 |
|---|---|---|---|---|
| 수렴 평균|r| (energy, pitch) | 0.598 | |r|≥0.5 | ✓ 충족 | 0.633 |
| 판별 평균|r| (wpm, duration) | 0.445 | 수렴보다 낮아야 | ✓ 충족 | 0.436 |
| 델타 (수렴−판별) | 0.153 | >0 | ✓ 양호 | 0.197 |
4.5. 가중치 민감도 분석 (n=998)
균등 가중치(0.25) 기준 모형과 6가지 대안 시나리오를 비교하였다. 모든 시나리오에서 r≥0.88 이상으로 유지되어 균등 가중치 설계가 안정적임을 확인하였다.
표 4-6. 가중치 시나리오 간 상관 분석 (n=998)
원본 참조표 9
| 시나리오 | 기본과의 r | MAD | 해석 |
|---|---|---|---|
| equal_25 (기본) | 1.0000 | 0.000 | 기준 모형 |
| pitch 중심 | 0.9280 | 0.023 | 안정 (r≥0.92) |
| energy 중심 | 0.9353 | 0.021 | 안정 (r≥0.93) |
| pitch+energy 동등강조 | 0.9381 | 0.021 | 안정 (r≥0.93) |
| wpm 낮춤 | 0.9478 | 0.019 | 안정 (r≥0.94) |
| wpm 제외 | 0.8792 | 0.032 | 보통 (r≥0.85 허용범위) |
| duration 제외 | 0.8778 | 0.025 | 보통 (r≥0.85 허용범위) |
주. 0413 기준 모든 시나리오 r≥0.88로 0402 보고(최저 r=0.841) 대비 전반적으로 향상됨.
그림 4-7. 가중치 시나리오별 r(좌) 및 MAD(우) 비교 (n=998)
4.6. 구간 프로파일 분석 — ANOVA (n=998)
stress_score의 33.3·66.7 백분위수를 기준으로 저·중·고 3구간으로 분류하고 일원배치 분산분석을 수행하였다. 모든 변수에서 대효과(η²≥0.13) 이상이 확인되었다.
표 4-7. 스트레스 구간별 ANOVA 결과 (n=998)
원본 참조표 10
| 변수 | F | p값 | η² | 효과크기 | 역할 |
|---|---|---|---|---|---|
| energy_mean | 260.48 | 1.06e-91 | 0.3436 | 대(large) | 수렴 핵심 |
| pitch_mean | 171.31 | 1.17e-64 | 0.2561 | 대(large) | 수렴 핵심 |
| wpm | 75.26 | 3.65e-31 | 0.1314 | 중(medium) | 보조 근거 |
| duration_sec | 75.18 | 3.91e-31 | 0.1313 | 중(medium) | 보조 근거 |
주. η²≥0.14=대효과(large), η²≥0.06=중효과(medium) (Cohen, 1988). df₁=2, df₂=995. 모든 변수 p<.001.
4.6.1. energy_mean — 구간별 분포
그림 4-8. 스트레스 구간별 energy_mean 분포 (F=260.48, p=1.06e-91, η²=0.344, 대효과)
4.6.2. pitch_mean — 구간별 분포
그림 4-9. 스트레스 구간별 pitch_mean 분포 (F=171.31, p=1.17e-64, η²=0.256, 대효과)
4.6.3. wpm — 구간별 분포
그림 4-10. 스트레스 구간별 wpm 분포 (F=75.26, p=3.65e-31, η²=0.131, 중효과)
4.6.4. duration_sec — 구간별 분포
그림 4-11. 스트레스 구간별 duration_sec 분포 (F=75.18, p=3.91e-31, η²=0.131, 중효과)
주. duration_sec는 저스트레스 구간에서 음수(-) 시뮬레이션 값이 나타나는 것은 정규분포 역산 방식의 한계이며, 실제 데이터에서는 양수만 존재함.
4.7. 다면적 타당도 검증 종합 요약 (n=998)
표 4-8. 내적 일관성 지표 및 다면적 타당도 검증 근거 요약 (n=998)
원본 참조표 11
| 지표 | 값 (n=998) | 기준 | 판정 | 0402 비교 |
|---|---|---|---|---|
| ① 수렴 타당도 — energy r | 0.6298 | r≥0.50 | ✓ 충족 | 0.672 |
| ① 수렴 타당도 — pitch r | 0.5669 | r≥0.50 | ✓ 충족 | 0.594 |
| 수렴 평균|r| | 0.598 | r≥0.50 | ✓ 충족 | 0.633 |
| ② 판별 평균|r| (수렴 대비) | 0.445 | 수렴보다 낮아야 | ✓ 충족 | 0.436 |
| 델타 (수렴−판별) | 0.153 | >0 | ✓ 양호 | 0.197 |
| ③ ANOVA η² — energy | 0.3436 | η²≥0.14 (대효과) | ✓ 대효과 | 0.424 |
| ③ ANOVA η² — pitch | 0.2561 | η²≥0.14 (대효과) | ✓ 대효과 | 0.177 |
| ④ 가중치 민감도 최저 r | 0.878 | r≥0.85 | ✓ 안정 | 0.841 |
주. 0413 기준 모든 지표에서 타당도 기준 충족 확인. 가중치 민감도 최저 r이 0402(0.841)→0413(0.878)로 개선됨.
5. 결론
본 연구는 국내 콜센터 VOC 음성 데이터 998건을 대상으로 음성 특징 기반 스트레스 지수를 설계하고, 다면적 구성 타당도 검증을 수행하였다. 세 차례의 단계적 보고를 통해 방법론을 정련하였으며, 최종 n=998 datasets0413 전역 기준 분석에서 다음 결론을 도출하였다.
원본 참조표 12
| 결론 | 내용 | 근거 지표 |
|---|---|---|
| ① | 수렴 타당도 확보 | energy r=0.630, pitch r=0.567 (기준 r≥0.50 충족) |
| ② | 타당도 구조 양호 | 수렴 평균|r|=0.598 > 판별 평균|r|=0.445 (델타=0.153) |
| ③ | 구간별 대효과 확인 | ANOVA: energy η²=0.344, pitch η²=0.256 (모두 대효과) |
| ④ | 가중치 설계 안정성 | 민감도 최저 r=0.878, 모든 시나리오 r≥0.85 허용범위 |
| ⑤ | 다중공선성 없음 | VIF 최대 1.10 (기준 VIF<5 충족) |
n=998 전체 데이터에서도 5가지 독립적 근거가 모두 타당도 기준을 충족하여 스트레스 지수 설계의 통계적 신뢰성이 확인되었다. 특히 duration_sec의 음(−)의 방향성(r=−0.514)은 단발성 고스트레스 발화 가설을 n=998 규모에서 실증적으로 뒷받침한다.
참고문헌
Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281-302.
Hansen, J. H. L., & Patil, S. (2007). Speech under stress: Analysis, modeling and recognition. Speaker Classification I, Springer.
McFee, B., et al. (2015). librosa: Audio and music signal analysis in Python. Proceedings of the 14th Python in Science Conference.
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric Theory (3rd ed.). McGraw-Hill.
Scherer, K. R. (2003). Vocal communication of emotion: A review of research paradigms. Speech Communication, 40(1-2), 227-256.
0413 분석 리포트 끝 | n=998 | datasets0413 | 생성: 2026-04-16
3. TO-BE n=1,000 재현성 분석
논문 작성용 상세 본문
TO-BE 분석은 동일 분석 체인을 n=1,000 운영 데이터에 적용해 STT, 음성 특징, 통화 단위 stress index의 재현 가능성을 확인하는 단계이다.
TO-BE의 목적은 본문 n=998 수치를 교체하는 것이 아니라, 자동화 파이프라인이 동일한 입력 규모에서 반복 실행되고 결과 슬롯이 일관되게 생성되는지를 검증하는 데 있다.
운영 콘솔은 STT·Features·Stress 각 단계의 행 수를 분리 표시하고, 서로 다른 결과 슬롯이 혼합되지 않도록 상태 코드를 관리한다.
TO-BE는 본문 통계를 교체하기 위한 재분석이 아니라, 자동화 파이프라인이 운영 규모에서 안정적으로 작동하는지를 검증하는 재현성 축이다.
각 단계의 행 수와 결과 슬롯을 분리해 관리함으로써 STT 1,000건, 특징 1,000건, 통화 단위 stress 1,000건이 같은 프로필에 속하는지 확인한다.
중단 후 재개와 성공 파일 건너뛰기는 대규모 음성 분석의 비용과 시간을 통제하는 운영적 재현성 요소이다.
분석 수치 및 결과표
표 3-1. TO-BE 단계별 결과
| 단계 | 건수 | 상태 |
|---|---|---|
| STT | 1,000 | READY |
| Acoustic Features | 1,000 | READY |
| Call-level Stress Index | 1,000 | READY |
표 3-2. AS-IS와 TO-BE 역할 구분
| 항목 | AS-IS | TO-BE |
|---|---|---|
| 분석 목적 | 본문 타당도 검증 | 재현성·자동화 검증 |
| 표본 | n=998 | n=1,000 |
| 본문 대체 | 기준 | 대체 금지 |
| 활용 | 논문 본문 | 부록/운영 검증 |
표 3-Q. 연구 질문
| 번호 | 연구 질문 |
|---|---|
| 1 | n=1,000 전체 파이프라인이 동일 단계와 행 수로 반복 가능한가? |
| 2 | AS-IS 본문 기준과 TO-BE 운영 결과를 혼합하지 않고 관리할 수 있는가? |
표 3-M. 분석 방법 및 도구
| 순서 | 방법/도구 |
|---|---|
| 1 | Profile n1000 |
| 2 | Resume/skip-success |
| 3 | STT-Feature-Stress lineage |
| 4 | Result-slot isolation |
| 5 | Deployment verification |
분석 그림 및 도식
원본 분석 산출물 연계
산출물 CSV: research_continuity/02_asis_same_method_reproduction/actual_asis_source_schema_v153.csv (행 13, 열 5)
전체 행 포함
| column | dtype | non_null | numeric | used_for_reproduction |
|---|---|---|---|---|
| wav_id | object | 998 | False | False |
| energy_mean | float64 | 998 | True | True |
| pitch_mean | float64 | 998 | True | True |
| duration_sec | float64 | 998 | True | True |
| word_cnt | int64 | 998 | True | False |
| wpm | float64 | 998 | True | True |
| text_len | int64 | 998 | True | False |
| z_energy_mean | float64 | 998 | True | False |
| z_pitch_mean | float64 | 998 | True | False |
| z_wpm | float64 | 998 | True | False |
| z_duration_sec | float64 | 998 | True | False |
| raw_stress | float64 | 998 | True | False |
| stress_score | float64 | 998 | True | True |
산출물 CSV: research_continuity/02_asis_same_method_reproduction/asis_implementation_inventory_v150.csv (행 7, 열 5)
전체 행 포함
| file | suffix | size_bytes | role_hint | sha256 |
|---|---|---|---|---|
| /mnt/data/v173_src/resources/asis/generate_report_0413.py | .py | 18988 | report | 89c748f25f157754e0ff4928365a757e581b5e9794d027a52d864a36d844f8d8 |
| /mnt/data/v173_src/resources/asis/00_scan_voc_role_columns.py | .py | 1916 | 64a52bee6f4b4876bf0c5d1bc55ed889fc6874ff7845494953bfdd7f368cf99a | |
| /mnt/data/v173_src/resources/asis/new_datasets_day.ps1 | .ps1 | 9498 | 7c6f27e2d1daaff5d630360d4082fae595bab7e58a9fec3854b1d55243acb787 | |
| /mnt/data/v173_src/resources/asis/run_datasets0406.py | .py | 41708 | 9a95ddcd037db6051ee1817a17cf83cc81acf5aba57c92f8b2e030e6ff4a5d03 | |
| /mnt/data/v173_src/resources/asis/run_datasets0413.py | .py | 27994 | 560ac740f0a86c88bd662f2b77747a3b8902e8630c1bd280f103b6192f716aad | |
| /mnt/data/v173_src/resources/asis/setup_datasets0413.ps1 | .ps1 | 3956 | e22fd41428472b20f626b3a35de34f2fb03f6348899f16da745706522be6e66a | |
| /mnt/data/v173_src/results/manuscript_text/learning/finetune_dataset_summary_ko.md | .md | 512 | 03e58d7fc7ff45bc7908306af8ee7bdceb5a0a475952471b5a0557cf8799d36d |
산출물 CSV: research_continuity/02_asis_same_method_reproduction/asis_implementation_inventory_v152.csv (행 7, 열 5)
전체 행 포함
| file | suffix | size_bytes | role_hint | sha256 |
|---|---|---|---|---|
| /mnt/data/v173_src/resources/asis/generate_report_0413.py | .py | 18988 | report | 89c748f25f157754e0ff4928365a757e581b5e9794d027a52d864a36d844f8d8 |
| /mnt/data/v173_src/resources/asis/00_scan_voc_role_columns.py | .py | 1916 | 64a52bee6f4b4876bf0c5d1bc55ed889fc6874ff7845494953bfdd7f368cf99a | |
| /mnt/data/v173_src/resources/asis/new_datasets_day.ps1 | .ps1 | 9498 | 7c6f27e2d1daaff5d630360d4082fae595bab7e58a9fec3854b1d55243acb787 | |
| /mnt/data/v173_src/resources/asis/run_datasets0406.py | .py | 41708 | 9a95ddcd037db6051ee1817a17cf83cc81acf5aba57c92f8b2e030e6ff4a5d03 | |
| /mnt/data/v173_src/resources/asis/run_datasets0413.py | .py | 27994 | 560ac740f0a86c88bd662f2b77747a3b8902e8630c1bd280f103b6192f716aad | |
| /mnt/data/v173_src/resources/asis/setup_datasets0413.ps1 | .ps1 | 3956 | e22fd41428472b20f626b3a35de34f2fb03f6348899f16da745706522be6e66a | |
| /mnt/data/v173_src/results/manuscript_text/learning/finetune_dataset_summary_ko.md | .md | 512 | 03e58d7fc7ff45bc7908306af8ee7bdceb5a0a475952471b5a0557cf8799d36d |
산출물 CSV: research_continuity/02_asis_same_method_reproduction/asis_implementation_inventory_v153.csv (행 7, 열 5)
전체 행 포함
| file | suffix | size_bytes | role_hint | sha256 |
|---|---|---|---|---|
| /mnt/data/v173_src/resources/asis/generate_report_0413.py | .py | 18988 | report | 89c748f25f157754e0ff4928365a757e581b5e9794d027a52d864a36d844f8d8 |
| /mnt/data/v173_src/resources/asis/00_scan_voc_role_columns.py | .py | 1916 | 64a52bee6f4b4876bf0c5d1bc55ed889fc6874ff7845494953bfdd7f368cf99a | |
| /mnt/data/v173_src/resources/asis/new_datasets_day.ps1 | .ps1 | 9498 | 7c6f27e2d1daaff5d630360d4082fae595bab7e58a9fec3854b1d55243acb787 | |
| /mnt/data/v173_src/resources/asis/run_datasets0406.py | .py | 41708 | 9a95ddcd037db6051ee1817a17cf83cc81acf5aba57c92f8b2e030e6ff4a5d03 | |
| /mnt/data/v173_src/resources/asis/run_datasets0413.py | .py | 27994 | 560ac740f0a86c88bd662f2b77747a3b8902e8630c1bd280f103b6192f716aad | |
| /mnt/data/v173_src/resources/asis/setup_datasets0413.ps1 | .ps1 | 3956 | e22fd41428472b20f626b3a35de34f2fb03f6348899f16da745706522be6e66a | |
| /mnt/data/v173_src/results/manuscript_text/learning/finetune_dataset_summary_ko.md | .md | 512 | 03e58d7fc7ff45bc7908306af8ee7bdceb5a0a475952471b5a0557cf8799d36d |
산출물 CSV: research_continuity/02_asis_same_method_reproduction/asis_implementation_inventory_v154.csv (행 7, 열 5)
전체 행 포함
| file | suffix | size_bytes | role_hint | sha256 |
|---|---|---|---|---|
| /mnt/data/v173_src/resources/asis/generate_report_0413.py | .py | 18988 | report | 89c748f25f157754e0ff4928365a757e581b5e9794d027a52d864a36d844f8d8 |
| /mnt/data/v173_src/resources/asis/00_scan_voc_role_columns.py | .py | 1916 | 64a52bee6f4b4876bf0c5d1bc55ed889fc6874ff7845494953bfdd7f368cf99a | |
| /mnt/data/v173_src/resources/asis/new_datasets_day.ps1 | .ps1 | 9498 | 7c6f27e2d1daaff5d630360d4082fae595bab7e58a9fec3854b1d55243acb787 | |
| /mnt/data/v173_src/resources/asis/run_datasets0406.py | .py | 41708 | 9a95ddcd037db6051ee1817a17cf83cc81acf5aba57c92f8b2e030e6ff4a5d03 | |
| /mnt/data/v173_src/resources/asis/run_datasets0413.py | .py | 27994 | 560ac740f0a86c88bd662f2b77747a3b8902e8630c1bd280f103b6192f716aad | |
| /mnt/data/v173_src/resources/asis/setup_datasets0413.ps1 | .ps1 | 3956 | e22fd41428472b20f626b3a35de34f2fb03f6348899f16da745706522be6e66a | |
| /mnt/data/v173_src/results/manuscript_text/learning/finetune_dataset_summary_ko.md | .md | 512 | 03e58d7fc7ff45bc7908306af8ee7bdceb5a0a475952471b5a0557cf8799d36d |
산출물 JSON: research_continuity/02_asis_same_method_reproduction/asis_same_method_lock.json
전체 행 포함
| 경로 | 값 |
|---|---|
| version | asis_0413_same_method_lock_v1 |
| principle | TO-BE 확장 산식과 섞지 않고 0413 기준 분석방법만 사용한다. |
| locked_items.sample_rule | AS-IS 0413 clean row 기준을 우선 재현한다. 행 비교는 반드시 공통 call_id/file_id 기준으로 수행한다. |
| locked_items.stt_rule | 0413 당시 STT 결과 또는 동일 모델/동일 전처리 조건을 사용한다. |
| locked_items.word_count_rule | 한국어 띄어쓰기/문장부호 처리 기준을 0413과 동일하게 고정한다. |
| locked_items.wpm_rule | word_count / duration 기준을 0413과 동일하게 고정한다. |
| locked_items.feature_rule | energy_mean, pitch_mean, duration_sec 추출 기준을 0413과 동일하게 고정한다. |
| locked_items.stress_rule | energy_mean, pitch_mean, wpm, duration_sec → z-score → 균등가중 → min-max 구조를 유지한다. |
| locked_items.normalization_rule | z-score 평균/표준편차와 min-max 기준은 0413 재현 기준으로 기록하고 재사용한다. |
| locked_items.anova_rule | Stress group 구간과 ANOVA/Tukey 기준을 0413과 동일하게 적용한다. |
| forbidden[0] | TO-BE 전용 컬럼을 동일방법 재현 데이터에 섞기 |
| forbidden[1] | n=1000 자동화 정규화 기준으로 0413 본문 결과를 덮어쓰기 |
| forbidden[2] | 행 번호 순서만으로 AS-IS/TO-BE 비교하기 |
| forbidden[3] | Kspon WER/CER 임시값을 최종 STT 검증값처럼 쓰기 |
산출물 CSV: research_continuity/02_asis_same_method_reproduction/available_input_candidates.csv (행 49, 열 5)
전체 행 포함
| file | relative | suffix | size_bytes | keyword_score |
|---|---|---|---|---|
| C:\AI\sci_voc_bot\results\tables\stress_index_feature_quality.csv | results\tables\stress_index_feature_quality.csv | .csv | 289 | 3 |
| C:\AI\sci_voc_bot\resources\asis\0413_voc_full_validation_report.html | resources\asis\0413_voc_full_validation_report.html | .html | 1274984 | 2 |
| C:\AI\sci_voc_bot\resources\tobe\asis_tobe_comparison_visual.png | resources\tobe\asis_tobe_comparison_visual.png | .png | 72449 | 2 |
| C:\AI\sci_voc_bot\results\tables\asis_tobe\asis_tobe_compare_manifest.json | results\tables\asis_tobe\asis_tobe_compare_manifest.json | .json | 2091 | 2 |
| C:\AI\sci_voc_bot\data\future_research\stt_reference_validation.csv | data\future_research\stt_reference_validation.csv | .csv | 100 | 2 |
| C:\AI\sci_voc_bot\results\stress_index\voc_stress_score_rebuilt.csv | results\stress_index\voc_stress_score_rebuilt.csv | .csv | 2067623 | 1 |
| C:\AI\sci_voc_bot\data\voc\features\voc_features.csv | data\voc\features\voc_features.csv | .csv | 1951496 | 1 |
| C:\AI\sci_voc_bot\results\figures\fig_stt_error_sensitivity.png | results\figures\fig_stt_error_sensitivity.png | .png | 65372 | 1 |
| C:\AI\sci_voc_bot\results\tables\asis_tobe\tobe_metric_source_trace.csv | results\tables\asis_tobe\tobe_metric_source_trace.csv | .csv | 48691 | 1 |
| C:\AI\sci_voc_bot\results\figures\fig_stt_reliability.png | results\figures\fig_stt_reliability.png | .png | 47126 | 1 |
| C:\AI\sci_voc_bot\results\figures\fig02_stress_distribution.png | results\figures\fig02_stress_distribution.png | .png | 45614 | 1 |
| C:\AI\sci_voc_bot\results\robustness\stt_error_sensitivity.csv | results\robustness\stt_error_sensitivity.csv | .csv | 1214 | 1 |
| C:\AI\sci_voc_bot\results\tables\stt_error_sensitivity.csv | results\tables\stt_error_sensitivity.csv | .csv | 1214 | 1 |
| C:\AI\sci_voc_bot\results\tables\scie_submission\kspon_validation_plan.csv | results\tables\scie_submission\kspon_validation_plan.csv | .csv | 610 | 1 |
| C:\AI\sci_voc_bot\results\runtime\resume\stt_checkpoint_voc_n1000_medium.json | results\runtime\resume\stt_checkpoint_voc_n1000_medium.json | .json | 573 | 1 |
| C:\AI\sci_voc_bot\results\tables\feature_descriptive_statistics.csv | results\tables\feature_descriptive_statistics.csv | .csv | 571 | 1 |
| C:\AI\sci_voc_bot\results\runtime\resume\stt_checkpoint_voc_n20_medium.json | results\runtime\resume\stt_checkpoint_voc_n20_medium.json | .json | 565 | 1 |
| C:\AI\sci_voc_bot\results\runtime\resume\stt_checkpoint_voc_n10_medium.json | results\runtime\resume\stt_checkpoint_voc_n10_medium.json | .json | 564 | 1 |
| C:\AI\sci_voc_bot\results\tables\scie_submission\statistical_validation_checklist.csv | results\tables\scie_submission\statistical_validation_checklist.csv | .csv | 554 | 1 |
| C:\AI\sci_voc_bot\results\runtime\resume\stt_checkpoint_voc_n1000_small.json | results\runtime\resume\stt_checkpoint_voc_n1000_small.json | .json | 525 | 1 |
| C:\AI\sci_voc_bot\results\runtime\resume\stt_checkpoint_voc_n100_small.json | results\runtime\resume\stt_checkpoint_voc_n100_small.json | .json | 520 | 1 |
| C:\AI\sci_voc_bot\results\runtime\resume\stt_checkpoint_voc_n10_small.json | results\runtime\resume\stt_checkpoint_voc_n10_small.json | .json | 517 | 1 |
| C:\AI\sci_voc_bot\results\tables\construct_validity_summary.csv | results\tables\construct_validity_summary.csv | .csv | 457 | 1 |
| C:\AI\sci_voc_bot\results\tables\correlation_validity.csv | results\tables\correlation_validity.csv | .csv | 344 | 1 |
| C:\AI\sci_voc_bot\results\tables\future_analysis\stt_wpm_reliability_sources.csv | results\tables\future_analysis\stt_wpm_reliability_sources.csv | .csv | 295 | 1 |
| C:\AI\sci_voc_bot\results\tables\future_analysis\criterion_validity_status.csv | results\tables\future_analysis\criterion_validity_status.csv | .csv | 202 | 1 |
| C:\AI\sci_voc_bot\results\tables\future_analysis\stt_wpm_reliability_status.csv | results\tables\future_analysis\stt_wpm_reliability_status.csv | .csv | 186 | 1 |
| C:\AI\sci_voc_bot\results\tables\future_analysis\stt_wpm_reliability_summary.csv | results\tables\future_analysis\stt_wpm_reliability_summary.csv | .csv | 159 | 1 |
| C:\AI\sci_voc_bot\results\tables\stt_reliability.csv | results\tables\stt_reliability.csv | .csv | 129 | 1 |
| C:\AI\sci_voc_bot\results\tables\validity_structure_summary.csv | results\tables\validity_structure_summary.csv | .csv | 112 | 1 |
| C:\AI\sci_voc_bot\results\tables\voc_stt_completeness.csv | results\tables\voc_stt_completeness.csv | .csv | 102 | 1 |
| C:\AI\sci_voc_bot\resources\tobe\to_be_analysis_dashboard.png | resources\tobe\to_be_analysis_dashboard.png | .png | 1243116 | 0 |
| C:\AI\sci_voc_bot\resources\tobe\to_be_분석_결과_대시보드.png | resources\tobe\to_be_분석_결과_대시보드.png | .png | 1243116 | 0 |
| C:\AI\sci_voc_bot\results\figures\fig05_weight_sensitivity.png | results\figures\fig05_weight_sensitivity.png | .png | 136183 | 0 |
| C:\AI\sci_voc_bot\results\figures\scatter_duration_sec.png | results\figures\scatter_duration_sec.png | .png | 99979 | 0 |
| C:\AI\sci_voc_bot\results\figures\scatter_pitch_mean.png | results\figures\scatter_pitch_mean.png | .png | 97954 | 0 |
| C:\AI\sci_voc_bot\results\figures\scatter_energy_mean.png | results\figures\scatter_energy_mean.png | .png | 97728 | 0 |
| C:\AI\sci_voc_bot\results\figures\fig04_bootstrap_ci.png | results\figures\fig04_bootstrap_ci.png | .png | 94330 | 0 |
| C:\AI\sci_voc_bot\resources\tobe\to_be_actual_visual_summary.png | resources\tobe\to_be_actual_visual_summary.png | .png | 83572 | 0 |
| C:\AI\sci_voc_bot\results\figures\scatter_wpm.png | results\figures\scatter_wpm.png | .png | 78334 | 0 |
| C:\AI\sci_voc_bot\results\figures\anova_pitch_mean.png | results\figures\anova_pitch_mean.png | .png | 74735 | 0 |
| C:\AI\sci_voc_bot\results\figures\anova_duration_sec.png | results\figures\anova_duration_sec.png | .png | 67942 | 0 |
| C:\AI\sci_voc_bot\results\figures\anova_energy_mean.png | results\figures\anova_energy_mean.png | .png | 63150 | 0 |
| C:\AI\sci_voc_bot\results\figures\fig01_framework.png | results\figures\fig01_framework.png | .png | 58870 | 0 |
| C:\AI\sci_voc_bot\results\figures\tukey_energy_mean.png | results\figures\tukey_energy_mean.png | .png | 55228 | 0 |
| C:\AI\sci_voc_bot\results\figures\tukey_duration_sec.png | results\figures\tukey_duration_sec.png | .png | 54390 | 0 |
| C:\AI\sci_voc_bot\results\figures\tukey_pitch_mean.png | results\figures\tukey_pitch_mean.png | .png | 54115 | 0 |
| C:\AI\sci_voc_bot\results\figures\anova_wpm.png | results\figures\anova_wpm.png | .png | 52828 | 0 |
| C:\AI\sci_voc_bot\results\figures\tukey_wpm.png | results\figures\tukey_wpm.png | .png | 51272 | 0 |
산출물 CSV: research_continuity/02_asis_same_method_reproduction/latest_actual_asis_source_schema_v150.csv (행 13, 열 5)
전체 행 포함
| column | dtype | non_null | numeric | used_for_reproduction |
|---|---|---|---|---|
| wav_id | object | 998 | False | False |
| energy_mean | float64 | 998 | True | True |
| pitch_mean | float64 | 998 | True | True |
| duration_sec | float64 | 998 | True | True |
| word_cnt | int64 | 998 | True | False |
| wpm | float64 | 998 | True | True |
| text_len | int64 | 998 | True | False |
| z_energy_mean | float64 | 998 | True | False |
| z_pitch_mean | float64 | 998 | True | False |
| z_wpm | float64 | 998 | True | False |
| z_duration_sec | float64 | 998 | True | False |
| raw_stress | float64 | 998 | True | False |
| stress_score | float64 | 998 | True | True |
산출물 JSON: research_continuity/02_asis_same_method_reproduction/latest_actual_same_method_normalization_candidate_params_v153.json
전체 행 포함
| 경로 | 값 |
|---|---|
| version | v153_formula_candidate_audit |
| zscore.energy_mean.mean | 0.06796451102669734 |
| zscore.energy_mean.std_ddof0 | 0.029216177626633043 |
| zscore.pitch_mean.mean | 229.83724494257334 |
| zscore.pitch_mean.std_ddof0 | 27.857178311341194 |
| zscore.wpm.mean | 82.77146038694565 |
| zscore.wpm.std_ddof0 | 19.41798158865202 |
| zscore.duration_sec.mean | 146.72020040080162 |
| zscore.duration_sec.std_ddof0 | 170.230623789939 |
| combined_min | -1.6939439702855632 |
| combined_max | 2.738439265232394 |
| feature_cols[0] | energy_mean |
| feature_cols[1] | pitch_mean |
| feature_cols[2] | wpm |
| feature_cols[3] | duration_sec |
| created_at | 2026-07-07 07:48:44 |
산출물 JSON: research_continuity/02_asis_same_method_reproduction/latest_actual_same_method_reproduction_manifest_v150.json
전체 행 포함
| 경로 | 값 |
|---|---|
| source_path | /mnt/data/v173_src/resources/asis/0413_final_locked/datasets0413_clean.csv |
| source_role | asis_stress |
| source_label | AS-IS 0413 Stress Index 결과 CSV |
| rows | 998 |
| cols | 13 |
| key_col | INDEX_ONLY |
| feature_cols_found[0] | energy_mean |
| feature_cols_found[1] | pitch_mean |
| feature_cols_found[2] | wpm |
| feature_cols_found[3] | duration_sec |
| stress_col_found | stress_score |
| version | v153_reference_locked_actual_reproduction |
| state | DONE_WITH_FORMULA_AUDIT_REVIEW |
| valid_rows | 998 |
| reproduction_mode | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT |
| stress_score_policy | reference_locked |
| formula_audit_decision | REVIEW_0413_NORMALIZATION_PARAMS_REQUIRED |
| message | 실제 AS-IS 데이터 기반 재현 산출물을 만들었습니다. stress_score는 reference lock으로 보호하고, 추정 산식 차이는 감사표로 분리했습니다. |
| outputs[0] | /mnt/data/v173_src/research_continuity/02_asis_same_method_reproduction/actual_asis_source_schema_v153.csv |
| outputs[1] | /mnt/data/v173_src/research_continuity/02_asis_same_method_reproduction/runs/20260707_074844/actual_same_method_reproduced_stress_index_v153.csv |
| outputs[2] | /mnt/data/v173_src/research_continuity/02_asis_same_method_reproduction/latest_actual_same_method_reproduced_stress_index_v153.csv |
| outputs[3] | /mnt/data/v173_src/research_continuity/02_asis_same_method_reproduction/actual_same_method_reproduced_stress_index_v150.csv |
| outputs[4] | /mnt/data/v173_src/research_continuity/02_asis_same_method_reproduction/runs/20260707_074844/actual_same_method_normalization_candidate_params_v153.json |
| outputs[5] | /mnt/data/v173_src/research_continuity/02_asis_same_method_reproduction/latest_actual_same_method_normalization_candidate_params_v153.json |
| outputs[6] | /mnt/data/v173_src/research_continuity/02_asis_same_method_reproduction/runs/20260707_074844/actual_same_method_reproduction_summary_v153.csv |
| outputs[7] | /mnt/data/v173_src/research_continuity/02_asis_same_method_reproduction/latest_actual_same_method_reproduction_summary_v153.csv |
| outputs[8] | /mnt/data/v173_src/research_continuity/02_asis_same_method_reproduction/latest_actual_same_method_reproduction_summary_v150.csv |
| outputs[9] | /mnt/data/v173_src/research_continuity/04_three_stage_comparison/stress_score_formula_audit_v153.csv |
| run_dir | /mnt/data/v173_src/research_continuity/02_asis_same_method_reproduction/runs/20260707_074844 |
산출물 JSON: research_continuity/02_asis_same_method_reproduction/latest_actual_same_method_reproduction_manifest_v152.json
전체 행 포함
| 경로 | 값 |
|---|---|
| state | DONE |
| source_path | C:\jupyter_env\datasets0413\04_final\datasets0413_clean.csv |
| source_role | asis_stress |
| source_label | AS-IS 0413 Stress Index 결과 CSV |
| source_read_only | True |
| run_dir | C:\AI\sci_voc_bot\research_continuity\02_asis_same_method_reproduction\runs\20260705_203636 |
| rows | 998 |
| cols | 13 |
| key_col | INDEX_ONLY |
| feature_cols_found[0] | energy_mean |
| feature_cols_found[1] | pitch_mean |
| feature_cols_found[2] | wpm |
| feature_cols_found[3] | duration_sec |
| stress_col_found | stress_score |
| original_data_policy | 원본 AS-IS 파일/폴더에는 쓰지 않고, 모든 산출물은 research_continuity/02_asis_same_method_reproduction/runs 아래에만 생성합니다. |
| valid_rows | 998 |
| reproduction_path | C:\AI\sci_voc_bot\research_continuity\02_asis_same_method_reproduction\runs\20260705_203636\actual_same_method_reproduced_stress_index_v150.csv |
| outputs[0] | C:\AI\sci_voc_bot\research_continuity\02_asis_same_method_reproduction\runs\20260705_203636\actual_asis_source_schema_v150.csv |
| outputs[1] | C:\AI\sci_voc_bot\research_continuity\02_asis_same_method_reproduction\runs\20260705_203636\actual_same_method_reproduced_stress_index_v150.csv |
| outputs[2] | C:\AI\sci_voc_bot\research_continuity\02_asis_same_method_reproduction\runs\20260705_203636\actual_same_method_normalization_params_v150.json |
| outputs[3] | C:\AI\sci_voc_bot\research_continuity\02_asis_same_method_reproduction\runs\20260705_203636\actual_same_method_reproduction_summary_v150.csv |
| outputs[4] | C:\AI\sci_voc_bot\research_continuity\02_asis_same_method_reproduction\runs\20260705_203636\actual_same_method_reproduction_manifest_v150.json |
| outputs[5] | C:\AI\sci_voc_bot\research_continuity\02_asis_same_method_reproduction\latest_actual_same_method_reproduction_manifest_v150.json |
| message | ENV/등록 경로의 실제 AS-IS 데이터를 읽기 전용으로 사용해 재현 폴더에 동일방법 산출물을 생성했습니다. |
| version | v152_guarded_actual_reproduction |
| synthetic_guard | PASS |
| min_asis_rows | 500 |
산출물 JSON: research_continuity/02_asis_same_method_reproduction/latest_actual_same_method_reproduction_manifest_v153.json
전체 행 포함
| 경로 | 값 |
|---|---|
| source_path | /mnt/data/v173_src/resources/asis/0413_final_locked/datasets0413_clean.csv |
| source_role | asis_stress |
| source_label | AS-IS 0413 Stress Index 결과 CSV |
| rows | 998 |
| cols | 13 |
| key_col | INDEX_ONLY |
| feature_cols_found[0] | energy_mean |
| feature_cols_found[1] | pitch_mean |
| feature_cols_found[2] | wpm |
| feature_cols_found[3] | duration_sec |
| stress_col_found | stress_score |
| version | v153_reference_locked_actual_reproduction |
| state | DONE_WITH_FORMULA_AUDIT_REVIEW |
| valid_rows | 998 |
| reproduction_mode | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT |
| stress_score_policy | reference_locked |
| formula_audit_decision | REVIEW_0413_NORMALIZATION_PARAMS_REQUIRED |
| message | 실제 AS-IS 데이터 기반 재현 산출물을 만들었습니다. stress_score는 reference lock으로 보호하고, 추정 산식 차이는 감사표로 분리했습니다. |
| outputs[0] | /mnt/data/v173_src/research_continuity/02_asis_same_method_reproduction/actual_asis_source_schema_v153.csv |
| outputs[1] | /mnt/data/v173_src/research_continuity/02_asis_same_method_reproduction/runs/20260707_074844/actual_same_method_reproduced_stress_index_v153.csv |
| outputs[2] | /mnt/data/v173_src/research_continuity/02_asis_same_method_reproduction/latest_actual_same_method_reproduced_stress_index_v153.csv |
| outputs[3] | /mnt/data/v173_src/research_continuity/02_asis_same_method_reproduction/actual_same_method_reproduced_stress_index_v150.csv |
| outputs[4] | /mnt/data/v173_src/research_continuity/02_asis_same_method_reproduction/runs/20260707_074844/actual_same_method_normalization_candidate_params_v153.json |
| outputs[5] | /mnt/data/v173_src/research_continuity/02_asis_same_method_reproduction/latest_actual_same_method_normalization_candidate_params_v153.json |
| outputs[6] | /mnt/data/v173_src/research_continuity/02_asis_same_method_reproduction/runs/20260707_074844/actual_same_method_reproduction_summary_v153.csv |
| outputs[7] | /mnt/data/v173_src/research_continuity/02_asis_same_method_reproduction/latest_actual_same_method_reproduction_summary_v153.csv |
| outputs[8] | /mnt/data/v173_src/research_continuity/02_asis_same_method_reproduction/latest_actual_same_method_reproduction_summary_v150.csv |
| outputs[9] | /mnt/data/v173_src/research_continuity/04_three_stage_comparison/stress_score_formula_audit_v153.csv |
| run_dir | /mnt/data/v173_src/research_continuity/02_asis_same_method_reproduction/runs/20260707_074844 |
산출물 JSON: research_continuity/02_asis_same_method_reproduction/latest_actual_same_method_reproduction_manifest_v154.json
전체 행 포함
| 경로 | 값 |
|---|---|
| source_path | /mnt/data/v173_src/resources/asis/0413_final_locked/datasets0413_clean.csv |
| source_role | asis_stress |
| source_label | AS-IS 0413 Stress Index 결과 CSV |
| rows | 998 |
| cols | 13 |
| key_col | INDEX_ONLY |
| feature_cols_found[0] | energy_mean |
| feature_cols_found[1] | pitch_mean |
| feature_cols_found[2] | wpm |
| feature_cols_found[3] | duration_sec |
| stress_col_found | stress_score |
| version | v154_reference_locked_reproduction |
| state | DONE_WITH_FORMULA_AUDIT_REVIEW |
| valid_rows | 998 |
| reproduction_mode | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT |
| stress_score_policy | reference_locked |
| formula_audit_decision | REVIEW_0413_NORMALIZATION_PARAMS_REQUIRED |
| message | 실제 AS-IS 데이터 기반 재현 산출물을 만들었습니다. stress_score는 reference lock으로 보호하고, 추정 산식 차이는 감사표로 분리했습니다. |
| outputs[0] | /mnt/data/v173_src/research_continuity/02_asis_same_method_reproduction/actual_asis_source_schema_v153.csv |
| outputs[1] | /mnt/data/v173_src/research_continuity/02_asis_same_method_reproduction/runs/20260707_074844/actual_same_method_reproduced_stress_index_v153.csv |
| outputs[2] | /mnt/data/v173_src/research_continuity/02_asis_same_method_reproduction/latest_actual_same_method_reproduced_stress_index_v153.csv |
| outputs[3] | /mnt/data/v173_src/research_continuity/02_asis_same_method_reproduction/actual_same_method_reproduced_stress_index_v150.csv |
| outputs[4] | /mnt/data/v173_src/research_continuity/02_asis_same_method_reproduction/runs/20260707_074844/actual_same_method_normalization_candidate_params_v153.json |
| outputs[5] | /mnt/data/v173_src/research_continuity/02_asis_same_method_reproduction/latest_actual_same_method_normalization_candidate_params_v153.json |
| outputs[6] | /mnt/data/v173_src/research_continuity/02_asis_same_method_reproduction/runs/20260707_074844/actual_same_method_reproduction_summary_v153.csv |
| outputs[7] | /mnt/data/v173_src/research_continuity/02_asis_same_method_reproduction/latest_actual_same_method_reproduction_summary_v153.csv |
| outputs[8] | /mnt/data/v173_src/research_continuity/02_asis_same_method_reproduction/latest_actual_same_method_reproduction_summary_v150.csv |
| outputs[9] | /mnt/data/v173_src/research_continuity/04_three_stage_comparison/stress_score_formula_audit_v153.csv |
| outputs[10] | /mnt/data/v173_src/research_continuity/02_asis_same_method_reproduction/runs/20260707_074844/actual_same_method_reproduction_manifest_v153.json |
| outputs[11] | /mnt/data/v173_src/research_continuity/02_asis_same_method_reproduction/latest_actual_same_method_reproduction_manifest_v153.json |
| run_dir | /mnt/data/v173_src/research_continuity/02_asis_same_method_reproduction/runs/20260707_074844 |
| deploy_label | SCI-VOC v154 · AS-IS reference lock · TO-BE n1000 자동화 검증 · Kspon ENV 연동 · missing 제외 산출 |
산출물 JSON: research_continuity/02_asis_same_method_reproduction/latest_actual_same_method_reproduction_manifest_v155.json
전체 행 포함
| 경로 | 값 |
|---|---|
| source_path | C:\jupyter_env\datasets0413\04_final\datasets0413_clean.csv |
| source_role | asis_stress |
| source_label | AS-IS 0413 Stress Index 결과 CSV |
| rows | 998 |
| cols | 13 |
| key_col | INDEX_ONLY |
| feature_cols_found[0] | energy_mean |
| feature_cols_found[1] | pitch_mean |
| feature_cols_found[2] | wpm |
| feature_cols_found[3] | duration_sec |
| stress_col_found | stress_score |
| version | v154_reference_locked_reproduction |
| state | DONE_WITH_FORMULA_AUDIT_REVIEW |
| valid_rows | 998 |
| reproduction_mode | REFERENCE_LOCKED_ASIS_STRESS_SCORE_WITH_FORMULA_AUDIT |
| stress_score_policy | reference_locked |
| formula_audit_decision | REVIEW_0413_NORMALIZATION_PARAMS_REQUIRED |
| message | 실제 AS-IS 데이터 기반 재현 산출물을 만들었습니다. stress_score는 reference lock으로 보호하고, 추정 산식 차이는 감사표로 분리했습니다. |
| outputs[0] | C:\AI\sci_voc_bot\research_continuity\02_asis_same_method_reproduction\actual_asis_source_schema_v153.csv |
| outputs[1] | C:\AI\sci_voc_bot\research_continuity\02_asis_same_method_reproduction\runs\20260705_230644\actual_same_method_reproduced_stress_index_v153.csv |
| outputs[2] | C:\AI\sci_voc_bot\research_continuity\02_asis_same_method_reproduction\latest_actual_same_method_reproduced_stress_index_v153.csv |
| outputs[3] | C:\AI\sci_voc_bot\research_continuity\02_asis_same_method_reproduction\actual_same_method_reproduced_stress_index_v150.csv |
| outputs[4] | C:\AI\sci_voc_bot\research_continuity\02_asis_same_method_reproduction\runs\20260705_230644\actual_same_method_normalization_candidate_params_v153.json |
| outputs[5] | C:\AI\sci_voc_bot\research_continuity\02_asis_same_method_reproduction\latest_actual_same_method_normalization_candidate_params_v153.json |
| outputs[6] | C:\AI\sci_voc_bot\research_continuity\02_asis_same_method_reproduction\runs\20260705_230644\actual_same_method_reproduction_summary_v153.csv |
| outputs[7] | C:\AI\sci_voc_bot\research_continuity\02_asis_same_method_reproduction\latest_actual_same_method_reproduction_summary_v153.csv |
| outputs[8] | C:\AI\sci_voc_bot\research_continuity\02_asis_same_method_reproduction\latest_actual_same_method_reproduction_summary_v150.csv |
| outputs[9] | C:\AI\sci_voc_bot\research_continuity\04_three_stage_comparison\stress_score_formula_audit_v153.csv |
| outputs[10] | C:\AI\sci_voc_bot\research_continuity\02_asis_same_method_reproduction\runs\20260705_230644\actual_same_method_reproduction_manifest_v153.json |
| outputs[11] | C:\AI\sci_voc_bot\research_continuity\02_asis_same_method_reproduction\latest_actual_same_method_reproduction_manifest_v153.json |
| run_dir | C:\AI\sci_voc_bot\research_continuity\02_asis_same_method_reproduction\runs\20260705_230644 |
| deploy_label | SCI-VOC v154 · AS-IS reference lock · TO-BE n1000 자동화 검증 · Kspon ENV 연동 · missing 제외 산출 |
결과 해석
TO-BE 1,000건은 분석 자동화와 운영 재현성을 보여주는 근거이며, 본문 결과와 직접적인 우열 비교 대상은 아니다.
한계 및 논문 반영 기준
AS-IS와 TO-BE의 전처리·버전·결과 슬롯 차이를 통제하지 않은 단순 수치 비교는 금지한다.
4. 실제 데이터 값 및 무결성 점검
논문 작성용 상세 본문
운영기에 표시되는 수치는 배포 시점의 JSON/CSV 결과를 기준으로 하며, 원천 후보 10,000건과 실제 분석 통화 1,000건을 구분한다.
화자분리 결과는 통화 파일 수, 세그먼트 수, 통화-화자 슬롯, 응답쌍, 디코딩 성공/실패 건수로 분리해 표시한다.
파일명에 .wav.wav가 보였던 문제는 표시용 문자열 처리 문제였으며, 실제 분석 파일과 세그먼트 결과의 무결성은 별도 진단표로 확인한다.
실제 데이터 값 검증은 단순한 화면 표시 확인이 아니라 파일 존재 여부, CSV 행 수, JSON 상태, 디코딩 성공 수, 세그먼트 수를 교차 확인하는 과정이다.
원천 후보 10,000건, 실제 분석 통화 1,000건, 화자 세그먼트 57,619건, 통화-화자 슬롯 2,000건은 서로 다른 분석 단위이다.
이 단위를 분리하지 않으면 2,000 슬롯을 2,000개의 원본 음성으로 오해하거나 세그먼트 수를 통화 수로 해석하는 오류가 발생한다.
분석 수치 및 결과표
표 4-1. 실제 데이터 계층
| 계층 | 건수 | 설명 |
|---|---|---|
| 원천 후보 | 10,000 | SOURCE_VOC_DIR 후보 |
| 실제 분석 통화 | 1,000 | TO-BE/화자분리 입력 |
| SPEAKER 세그먼트 | 57,619 | pyannote 결과 |
| 통화-화자 슬롯 | 2,000 | 1,000×2 |
| S0→S1 응답쌍 | 28,149 | 시간 순서 후보 |
표 4-2. 오디오 처리 무결성
| 항목 | 건수 | 판정 |
|---|---|---|
| 디코딩 성공 | 1,000 | 정상 |
| 디코딩 실패 | 0 | 0이면 정상 |
| energy 계산 완료 | 57,611 | 일부 극단 단구간 제외 |
| pitch 계산 완료 | 53,098 | voiced 구간 기준 |
표 4-Q. 연구 질문
| 번호 | 연구 질문 |
|---|---|
| 1 | 운영기 수치가 실제 파일·행·세그먼트 상태와 일치하는가? |
| 2 | 표시 오류와 실제 데이터 오류를 구분할 수 있는가? |
표 4-M. 분석 방법 및 도구
| 순서 | 방법/도구 |
|---|---|
| 1 | File diagnostics |
| 2 | Row count audit |
| 3 | Hash/size inventory |
| 4 | Decode success check |
| 5 | Public/deploy verification |
분석 그림 및 도식
원본 분석 산출물 연계
산출물 CSV: research_continuity/24_speaker_actual_diarization_v181/speaker_actual_file_diagnostics_v189.csv (행 1,000, 열 8)
대용량 표 요약·앞/뒤 표본 포함, 원본은 데이터 ZIP 수록
| file | duration_sec | sample_rate | channels | speaker_count | segment_count | status | error |
|---|---|---|---|---|---|---|---|
| 00__350002022300000.wav | 531.81 | 8000 | 1 | 2 | 194 | OK_2_SPEAKERS | |
| 00__350002022300001.wav | 148.71 | 8000 | 1 | 2 | 61 | OK_2_SPEAKERS | |
| 00__350002022300002.wav | 65.4 | 8000 | 1 | 2 | 36 | OK_2_SPEAKERS | |
| 00__350002022300003.wav | 54.3 | 8000 | 1 | 2 | 30 | OK_2_SPEAKERS | |
| 00__350002022300004.wav | 52.98 | 8000 | 1 | 2 | 31 | OK_2_SPEAKERS | |
| 00__350002022300005.wav | 30.96 | 8000 | 1 | 2 | 13 | OK_2_SPEAKERS | |
| 00__350002022300006.wav | 100.08 | 8000 | 1 | 2 | 53 | OK_2_SPEAKERS | |
| 00__350002022300007.wav | 222.0 | 8000 | 1 | 2 | 63 | OK_2_SPEAKERS | |
| 00__350002022300008.wav | 77.01 | 8000 | 1 | 2 | 24 | OK_2_SPEAKERS | |
| 00__350002022300009.wav | 72.12 | 8000 | 1 | 2 | 35 | OK_2_SPEAKERS | |
| 00__350002022300010.wav | 6.57 | 8000 | 1 | 1 | 2 | PARTIAL_1_SPEAKER | single speaker label only |
| 00__350002022300011.wav | 158.19 | 8000 | 1 | 2 | 49 | OK_2_SPEAKERS | |
| 00__350002022300012.wav | 235.17 | 8000 | 1 | 2 | 97 | OK_2_SPEAKERS | |
| 00__350002022300013.wav | 321.0 | 8000 | 1 | 2 | 150 | OK_2_SPEAKERS | |
| 00__350002022300014.wav | 47.67 | 8000 | 1 | 2 | 23 | OK_2_SPEAKERS | |
| 00__350002022300015.wav | 48.96 | 8000 | 1 | 2 | 19 | OK_2_SPEAKERS | |
| 00__350002022300016.wav | 22.5 | 8000 | 1 | 2 | 13 | OK_2_SPEAKERS | |
| 00__350002022300017.wav | 24.3 | 8000 | 1 | 2 | 12 | OK_2_SPEAKERS | |
| 00__350002022300018.wav | 521.7 | 8000 | 1 | 2 | 227 | OK_2_SPEAKERS | |
| 00__350002022300019.wav | 14.94 | 8000 | 1 | 2 | 10 | OK_2_SPEAKERS | |
| 00__350002022300020.wav | 1038.18 | 8000 | 1 | 2 | 377 | OK_2_SPEAKERS | |
| 00__350002022300021.wav | 524.82 | 8000 | 1 | 2 | 178 | OK_2_SPEAKERS | |
| 00__350002022300022.wav | 27.69 | 8000 | 1 | 2 | 12 | OK_2_SPEAKERS | |
| 00__350002022300023.wav | 299.82 | 8000 | 1 | 2 | 119 | OK_2_SPEAKERS | |
| 00__350002022300024.wav | 31.59 | 8000 | 1 | 2 | 19 | OK_2_SPEAKERS | |
| ... 중간 950행은 전체 데이터 부록 ZIP에 원본으로 포함 ... | |||||||
| 09__350002022300975.wav | 26.1 | 8000 | 1 | 2 | 11 | OK_2_SPEAKERS | |
| 09__350002022300976.wav | 123.42 | 8000 | 1 | 2 | 55 | OK_2_SPEAKERS | |
| 09__350002022300977.wav | 120.75 | 8000 | 1 | 2 | 53 | OK_2_SPEAKERS | |
| 09__350002022300978.wav | 73.59 | 8000 | 1 | 2 | 35 | OK_2_SPEAKERS | |
| 09__350002022300979.wav | 5.22 | 8000 | 1 | 1 | 2 | PARTIAL_1_SPEAKER | single speaker label only |
| 09__350002022300980.wav | 45.24 | 8000 | 1 | 2 | 28 | OK_2_SPEAKERS | |
| 09__350002022300981.wav | 150.36 | 8000 | 1 | 2 | 60 | OK_2_SPEAKERS | |
| 09__350002022300982.wav | 7.44 | 8000 | 1 | 1 | 1 | PARTIAL_1_SPEAKER | single speaker label only |
| 09__350002022300983.wav | 204.45 | 8000 | 1 | 2 | 104 | OK_2_SPEAKERS | |
| 09__350002022300984.wav | 6.84 | 8000 | 1 | 1 | 1 | PARTIAL_1_SPEAKER | single speaker label only |
| 09__350002022300985.wav | 47.94 | 8000 | 1 | 2 | 18 | OK_2_SPEAKERS | |
| 09__350002022300986.wav | 173.46 | 8000 | 1 | 2 | 83 | OK_2_SPEAKERS | |
| 09__350002022300987.wav | 174.3 | 8000 | 1 | 2 | 68 | OK_2_SPEAKERS | |
| 09__350002022300988.wav | 164.52 | 8000 | 1 | 2 | 58 | OK_2_SPEAKERS | |
| 09__350002022300989.wav | 523.2 | 8000 | 1 | 2 | 200 | OK_2_SPEAKERS | |
| 09__350002022300990.wav | 176.52 | 8000 | 1 | 2 | 67 | OK_2_SPEAKERS | |
| 09__350002022300991.wav | 149.64 | 8000 | 1 | 2 | 60 | OK_2_SPEAKERS | |
| 09__350002022300992.wav | 62.97 | 8000 | 1 | 2 | 26 | OK_2_SPEAKERS | |
| 09__350002022300993.wav | 32.94 | 8000 | 1 | 2 | 10 | OK_2_SPEAKERS | |
| 09__350002022300994.wav | 16.5 | 8000 | 1 | 2 | 6 | OK_2_SPEAKERS | |
| 09__350002022300995.wav | 156.12 | 8000 | 1 | 2 | 79 | OK_2_SPEAKERS | |
| 09__350002022300996.wav | 60.09 | 8000 | 1 | 2 | 45 | OK_2_SPEAKERS | |
| 09__350002022300997.wav | 107.4 | 8000 | 1 | 2 | 59 | OK_2_SPEAKERS | |
| 09__350002022300998.wav | 45.84 | 8000 | 1 | 2 | 14 | OK_2_SPEAKERS | |
| 09__350002022300999.wav | 35.52 | 8000 | 1 | 2 | 12 | OK_2_SPEAKERS |
산출물 CSV: research_continuity/24_speaker_actual_diarization_v181/speaker_actual_file_diagnostics_v199.csv (행 1,000, 열 8)
대용량 표 요약·앞/뒤 표본 포함, 원본은 데이터 ZIP 수록
| file | duration_sec | sample_rate | channels | speaker_count | segment_count | status | error |
|---|---|---|---|---|---|---|---|
| 00__350002022300000.wav | 531.81 | 8000 | 1 | 2 | 194 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 00__350002022300001.wav | 148.71 | 8000 | 1 | 2 | 61 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 00__350002022300002.wav | 65.4 | 8000 | 1 | 2 | 36 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 00__350002022300003.wav | 54.3 | 8000 | 1 | 2 | 30 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 00__350002022300004.wav | 52.98 | 8000 | 1 | 2 | 31 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 00__350002022300005.wav | 30.96 | 8000 | 1 | 2 | 13 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 00__350002022300006.wav | 100.08 | 8000 | 1 | 2 | 53 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 00__350002022300007.wav | 222.0 | 8000 | 1 | 2 | 63 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 00__350002022300008.wav | 77.01 | 8000 | 1 | 2 | 24 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 00__350002022300009.wav | 72.12 | 8000 | 1 | 2 | 35 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 00__350002022300010.wav | 6.57 | 8000 | 1 | 1 | 2 | PARTIAL_1_SPEAKER | single speaker label only | soundfile:LibsndfileError;wave:Error |
| 00__350002022300011.wav | 158.19 | 8000 | 1 | 2 | 49 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 00__350002022300012.wav | 235.17 | 8000 | 1 | 2 | 97 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 00__350002022300013.wav | 321.0 | 8000 | 1 | 2 | 150 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 00__350002022300014.wav | 47.67 | 8000 | 1 | 2 | 23 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 00__350002022300015.wav | 48.96 | 8000 | 1 | 2 | 19 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 00__350002022300016.wav | 22.5 | 8000 | 1 | 2 | 13 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 00__350002022300017.wav | 24.3 | 8000 | 1 | 2 | 12 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 00__350002022300018.wav | 521.7 | 8000 | 1 | 2 | 227 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 00__350002022300019.wav | 14.94 | 8000 | 1 | 2 | 10 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 00__350002022300020.wav | 1038.18 | 8000 | 1 | 2 | 377 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 00__350002022300021.wav | 524.82 | 8000 | 1 | 2 | 178 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 00__350002022300022.wav | 27.69 | 8000 | 1 | 2 | 12 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 00__350002022300023.wav | 299.82 | 8000 | 1 | 2 | 119 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 00__350002022300024.wav | 31.59 | 8000 | 1 | 2 | 19 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| ... 중간 950행은 전체 데이터 부록 ZIP에 원본으로 포함 ... | |||||||
| 09__350002022300975.wav | 26.1 | 8000 | 1 | 2 | 11 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 09__350002022300976.wav | 123.42 | 8000 | 1 | 2 | 55 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 09__350002022300977.wav | 120.75 | 8000 | 1 | 2 | 53 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 09__350002022300978.wav | 73.59 | 8000 | 1 | 2 | 35 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 09__350002022300979.wav | 5.22 | 8000 | 1 | 1 | 2 | PARTIAL_1_SPEAKER | single speaker label only | soundfile:LibsndfileError;wave:Error |
| 09__350002022300980.wav | 45.24 | 8000 | 1 | 2 | 28 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 09__350002022300981.wav | 150.36 | 8000 | 1 | 2 | 60 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 09__350002022300982.wav | 7.44 | 8000 | 1 | 1 | 1 | PARTIAL_1_SPEAKER | single speaker label only | soundfile:LibsndfileError;wave:Error |
| 09__350002022300983.wav | 204.45 | 8000 | 1 | 2 | 104 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 09__350002022300984.wav | 6.84 | 8000 | 1 | 1 | 1 | PARTIAL_1_SPEAKER | single speaker label only | soundfile:LibsndfileError;wave:Error |
| 09__350002022300985.wav | 47.94 | 8000 | 1 | 2 | 18 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 09__350002022300986.wav | 173.46 | 8000 | 1 | 2 | 83 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 09__350002022300987.wav | 174.3 | 8000 | 1 | 2 | 68 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 09__350002022300988.wav | 164.52 | 8000 | 1 | 2 | 58 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 09__350002022300989.wav | 523.2 | 8000 | 1 | 2 | 200 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 09__350002022300990.wav | 176.52 | 8000 | 1 | 2 | 67 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 09__350002022300991.wav | 149.64 | 8000 | 1 | 2 | 60 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 09__350002022300992.wav | 62.97 | 8000 | 1 | 2 | 26 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 09__350002022300993.wav | 32.94 | 8000 | 1 | 2 | 10 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 09__350002022300994.wav | 16.5 | 8000 | 1 | 2 | 6 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 09__350002022300995.wav | 156.12 | 8000 | 1 | 2 | 79 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 09__350002022300996.wav | 60.09 | 8000 | 1 | 2 | 45 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 09__350002022300997.wav | 107.4 | 8000 | 1 | 2 | 59 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 09__350002022300998.wav | 45.84 | 8000 | 1 | 2 | 14 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
| 09__350002022300999.wav | 35.52 | 8000 | 1 | 2 | 12 | OK_2_SPEAKERS | soundfile:LibsndfileError;wave:Error |
산출물 CSV: research_continuity/24_speaker_actual_diarization_v181/speaker_actual_file_diagnostics_v200.csv (행 1,000, 열 8)
대용량 표 요약·앞/뒤 표본 포함, 원본은 데이터 ZIP 수록
| file | duration_sec | sample_rate | channels | speaker_count | segment_count | status | error |
|---|---|---|---|---|---|---|---|
| 00__350002022300000.wav | 531.81 | 8000 | 1 | 2 | 194 | OK_2_SPEAKERS | |
| 00__350002022300001.wav | 148.71 | 8000 | 1 | 2 | 61 | OK_2_SPEAKERS | |
| 00__350002022300002.wav | 65.4 | 8000 | 1 | 2 | 36 | OK_2_SPEAKERS | |
| 00__350002022300003.wav | 54.3 | 8000 | 1 | 2 | 30 | OK_2_SPEAKERS | |
| 00__350002022300004.wav | 52.98 | 8000 | 1 | 2 | 31 | OK_2_SPEAKERS | |
| 00__350002022300005.wav | 30.96 | 8000 | 1 | 2 | 13 | OK_2_SPEAKERS | |
| 00__350002022300006.wav | 100.08 | 8000 | 1 | 2 | 53 | OK_2_SPEAKERS | |
| 00__350002022300007.wav | 222.0 | 8000 | 1 | 2 | 63 | OK_2_SPEAKERS | |
| 00__350002022300008.wav | 77.01 | 8000 | 1 | 2 | 24 | OK_2_SPEAKERS | |
| 00__350002022300009.wav | 72.12 | 8000 | 1 | 2 | 35 | OK_2_SPEAKERS | |
| 00__350002022300010.wav | 6.57 | 8000 | 1 | 1 | 2 | PARTIAL_1_SPEAKER | single speaker label only |
| 00__350002022300011.wav | 158.19 | 8000 | 1 | 2 | 49 | OK_2_SPEAKERS | |
| 00__350002022300012.wav | 235.17 | 8000 | 1 | 2 | 97 | OK_2_SPEAKERS | |
| 00__350002022300013.wav | 321.0 | 8000 | 1 | 2 | 150 | OK_2_SPEAKERS | |
| 00__350002022300014.wav | 47.67 | 8000 | 1 | 2 | 23 | OK_2_SPEAKERS | |
| 00__350002022300015.wav | 48.96 | 8000 | 1 | 2 | 19 | OK_2_SPEAKERS | |
| 00__350002022300016.wav | 22.5 | 8000 | 1 | 2 | 13 | OK_2_SPEAKERS | |
| 00__350002022300017.wav | 24.3 | 8000 | 1 | 2 | 12 | OK_2_SPEAKERS | |
| 00__350002022300018.wav | 521.7 | 8000 | 1 | 2 | 227 | OK_2_SPEAKERS | |
| 00__350002022300019.wav | 14.94 | 8000 | 1 | 2 | 10 | OK_2_SPEAKERS | |
| 00__350002022300020.wav | 1038.18 | 8000 | 1 | 2 | 377 | OK_2_SPEAKERS | |
| 00__350002022300021.wav | 524.82 | 8000 | 1 | 2 | 178 | OK_2_SPEAKERS | |
| 00__350002022300022.wav | 27.69 | 8000 | 1 | 2 | 12 | OK_2_SPEAKERS | |
| 00__350002022300023.wav | 299.82 | 8000 | 1 | 2 | 119 | OK_2_SPEAKERS | |
| 00__350002022300024.wav | 31.59 | 8000 | 1 | 2 | 19 | OK_2_SPEAKERS | |
| ... 중간 950행은 전체 데이터 부록 ZIP에 원본으로 포함 ... | |||||||
| 09__350002022300975.wav | 26.1 | 8000 | 1 | 2 | 11 | OK_2_SPEAKERS | |
| 09__350002022300976.wav | 123.42 | 8000 | 1 | 2 | 55 | OK_2_SPEAKERS | |
| 09__350002022300977.wav | 120.75 | 8000 | 1 | 2 | 53 | OK_2_SPEAKERS | |
| 09__350002022300978.wav | 73.59 | 8000 | 1 | 2 | 35 | OK_2_SPEAKERS | |
| 09__350002022300979.wav | 5.22 | 8000 | 1 | 1 | 2 | PARTIAL_1_SPEAKER | single speaker label only |
| 09__350002022300980.wav | 45.24 | 8000 | 1 | 2 | 28 | OK_2_SPEAKERS | |
| 09__350002022300981.wav | 150.36 | 8000 | 1 | 2 | 60 | OK_2_SPEAKERS | |
| 09__350002022300982.wav | 7.44 | 8000 | 1 | 1 | 1 | PARTIAL_1_SPEAKER | single speaker label only |
| 09__350002022300983.wav | 204.45 | 8000 | 1 | 2 | 104 | OK_2_SPEAKERS | |
| 09__350002022300984.wav | 6.84 | 8000 | 1 | 1 | 1 | PARTIAL_1_SPEAKER | single speaker label only |
| 09__350002022300985.wav | 47.94 | 8000 | 1 | 2 | 18 | OK_2_SPEAKERS | |
| 09__350002022300986.wav | 173.46 | 8000 | 1 | 2 | 83 | OK_2_SPEAKERS | |
| 09__350002022300987.wav | 174.3 | 8000 | 1 | 2 | 68 | OK_2_SPEAKERS | |
| 09__350002022300988.wav | 164.52 | 8000 | 1 | 2 | 58 | OK_2_SPEAKERS | |
| 09__350002022300989.wav | 523.2 | 8000 | 1 | 2 | 200 | OK_2_SPEAKERS | |
| 09__350002022300990.wav | 176.52 | 8000 | 1 | 2 | 67 | OK_2_SPEAKERS | |
| 09__350002022300991.wav | 149.64 | 8000 | 1 | 2 | 60 | OK_2_SPEAKERS | |
| 09__350002022300992.wav | 62.97 | 8000 | 1 | 2 | 26 | OK_2_SPEAKERS | |
| 09__350002022300993.wav | 32.94 | 8000 | 1 | 2 | 10 | OK_2_SPEAKERS | |
| 09__350002022300994.wav | 16.5 | 8000 | 1 | 2 | 6 | OK_2_SPEAKERS | |
| 09__350002022300995.wav | 156.12 | 8000 | 1 | 2 | 79 | OK_2_SPEAKERS | |
| 09__350002022300996.wav | 60.09 | 8000 | 1 | 2 | 45 | OK_2_SPEAKERS | |
| 09__350002022300997.wav | 107.4 | 8000 | 1 | 2 | 59 | OK_2_SPEAKERS | |
| 09__350002022300998.wav | 45.84 | 8000 | 1 | 2 | 14 | OK_2_SPEAKERS | |
| 09__350002022300999.wav | 35.52 | 8000 | 1 | 2 | 12 | OK_2_SPEAKERS |
산출물 CSV: results/tables/data_quality_action_plan.csv (행 3, 열 7)
전체 행 포함
| priority | action_code | action_name | trigger_items | reason | auto_executable | command_hint |
|---|---|---|---|---|---|---|
| 1 | FIX_VOC_STT_WPM | VOC STT/WPM 자동 복구 | VOC transcript column; VOC word_cnt; VOC text_len; VOC wpm; Stress index z_wpm | 전사문, 단어 수, WPM 또는 z_wpm이 비정상입니다. VOC STT부터 다시 돌려야 합니다. | YES | python src/auto_remediate.py --fix-voc-stt-wpm --model medium --limit <N> --overwrite-stt |
| 3 | REVIEW_FEATURE_QUALITY | 상수/결측 변수 해석 제한 표시 | Feature quality table | 상수 또는 결측 처리된 변수가 있습니다. 자동 복구보다 STT/원본 데이터 확인이 우선입니다. | PARTIAL | 먼저 FIX_VOC_STT_WPM 실행. 이후에도 남으면 해당 변수는 본문에서 보조/제외로 표시 |
| 4 | RUN_LARGER_SAMPLE | 표본 수 확대 재실행 | VOC feature sample size | 현재는 테스트/파일럿 표본입니다. SCI 원고용은 500개 이상, 가능하면 전체/998건으로 재실행해야 합니다. | YES | 대시보드 테스트 개수 500/1000/전체 선택 후 전체 파이프라인 또는 자동 조치 실행 |
산출물 CSV: results/tables/deploy_result_integrity.csv (행 22, 열 7)
전체 행 포함
| artifact | source_type | path | exists | rows_or_exists | mtime | size_kb |
|---|---|---|---|---|---|---|
| voc_stt_small | root | C:\AI\sci_voc_bot\data\voc\stt\voc_whisper_small_results.csv | True | 1000 | 2026-07-02 19:51:08 | 1925.4 |
| voc_stt_small | snapshot | C:\AI\sci_voc_bot\results_by_count\n1000\snapshot\data\voc\stt\voc_whisper_small_results.csv | True | 1000 | 2026-07-02 19:51:08 | 1925.4 |
| voc_stt_small | branch02 | C:\AI\sci_voc_bot\analysis_branches\02_tobe_recalc_n1000\data\voc\stt\voc_whisper_small_results.csv | True | 1000 | 2026-07-02 19:51:08 | 1925.4 |
| voc_stt_medium | root | C:\AI\sci_voc_bot\data\voc\stt\voc_whisper_medium_results.csv | True | 8 | 2026-07-02 13:25:39 | 6.5 |
| voc_stt_medium | snapshot | C:\AI\sci_voc_bot\results_by_count\n1000\snapshot\data\voc\stt\voc_whisper_medium_results.csv | True | 8 | 2026-07-02 13:25:39 | 6.5 |
| voc_stt_medium | branch02 | C:\AI\sci_voc_bot\analysis_branches\02_tobe_recalc_n1000\data\voc\stt\voc_whisper_medium_results.csv | True | 8 | 2026-07-02 13:25:39 | 6.5 |
| features | root | C:\AI\sci_voc_bot\data\voc\features\voc_features.csv | True | 1000 | 2026-07-03 17:11:46 | 1905.8 |
| features | branch02 | C:\AI\sci_voc_bot\analysis_branches\02_tobe_recalc_n1000\data\voc\features\voc_features.csv | True | 1000 | 2026-07-03 17:11:46 | 1905.8 |
| features | snapshot | C:\AI\sci_voc_bot\results_by_count\n1000\snapshot\data\voc\features\voc_features.csv | True | 1000 | 2026-07-03 17:11:46 | 1905.8 |
| stress | root | C:\AI\sci_voc_bot\results\stress_index\voc_stress_score_rebuilt.csv | True | 1000 | 2026-07-03 17:11:49 | 2019.2 |
| stress | root_alt_features | C:\AI\sci_voc_bot\data\voc\features\voc_stress_score_rebuilt.csv | False | 0 | 0.0 | |
| stress | root_alt_tables | C:\AI\sci_voc_bot\results\tables\voc_stress_score_rebuilt.csv | False | 0 | 0.0 | |
| stress | branch02 | C:\AI\sci_voc_bot\analysis_branches\02_tobe_recalc_n1000\results\stress_index\voc_stress_score_rebuilt.csv | True | 1000 | 2026-07-03 17:11:49 | 2019.2 |
| stress | branch02_alt | C:\AI\sci_voc_bot\analysis_branches\02_tobe_recalc_n1000\data\voc\features\voc_stress_score_rebuilt.csv | False | 0 | 0.0 | |
| stress | snapshot | C:\AI\sci_voc_bot\results_by_count\n1000\snapshot\results\stress_index\voc_stress_score_rebuilt.csv | True | 1000 | 2026-07-03 17:11:49 | 2019.2 |
| stress | snapshot_alt | C:\AI\sci_voc_bot\results_by_count\n1000\snapshot\data\voc\features\voc_stress_score_rebuilt.csv | False | 0 | 0.0 | |
| aiis_final_status | root | C:\AI\sci_voc_bot\results\runtime\aiis_final_diff_status.json | True | 1 | 2026-07-03 19:00:09 | 0.9 |
| aiis_final_status | snapshot | C:\AI\sci_voc_bot\results_by_count\n1000\snapshot\results\runtime\aiis_final_diff_status.json | False | 0 | 0.0 | |
| result_similarity_status | root | C:\AI\sci_voc_bot\results\runtime\result_similarity_status.json | True | 1 | 2026-07-04 14:23:48 | 0.6 |
| result_similarity_status | snapshot | C:\AI\sci_voc_bot\results_by_count\n1000\snapshot\results\runtime\result_similarity_status.json | False | 0 | 0.0 | |
| scie_pack | root | C:\AI\sci_voc_bot\reports\scie_submission\scie_submission_readiness_pack.docx | True | 1 | 2026-07-03 13:20:41 | 39.4 |
| scie_pack | branch03 | C:\AI\sci_voc_bot\analysis_branches\03_paper_safe_asis_main\reports\scie_submission\scie_submission_readiness_pack.docx | True | 1 | 2026-07-03 13:20:41 | 39.4 |
산출물 CSV: results/tables/final_paper_quality_check.csv (행 53, 열 6)
전체 행 포함
| section_id | section_title | issue_type | matched_text | severity | suggested_fix |
|---|---|---|---|---|---|
| 01_abstract | 초록 및 핵심 기여 | raw_figure_filename | 표로서의 타당성 근거를 확보하는 데 있다. 제안된 프레임워크는 그림 1(fig01_framework.png)에 도식화되어 있으며, 수집된 음성 데이터에 대해 자동 음성 인식(ST | medium | 그림 파일명이 본문에 그대로 남아 있음 |
| 01_abstract | 초록 및 핵심 기여 | raw_figure_filename | stt_reliability.csv ## 이 목차에 포함된 이미지 - fig01_framework.png | medium | 그림 파일명이 본문에 그대로 남아 있음 |
| 01_abstract | 초록 및 핵심 기여 | raw_csv_filename | 탐색적 도구로서의 가능성을 보여준다. ## 이 목차에 포함된 표 - construct_validity_summary.csv - stt_reliability.csv ## 이 목차에 포함된 이미지 | medium | CSV 파일명이 본문에 그대로 남아 있음 |
| 01_abstract | 초록 및 핵심 기여 | raw_csv_filename | 함된 표 - construct_validity_summary.csv - stt_reliability.csv ## 이 목차에 포함된 이미지 - fig01_framework.png | medium | CSV 파일명이 본문에 그대로 남아 있음 |
| 01_abstract | 초록 및 핵심 기여 | raw_missing_value | 당도(convergent validity)와 변별 타당도(discriminant validity)를 평가하였다. 수렴 타당도 지표로서 energy m | high | 결측/None 표기가 본문에 남아 있음 |
| 01_abstract | 초록 및 핵심 기여 | diagnosis_claim | 관찰되어 지수의 해석에 주의가 필요함을 시사한다. 이는 제안된 지수가 임상적 스트레스 진단이나 예측 모형으로 직접 사용되기보다는, 음성 특징 기반 스트레스 평가의 | medium | 진단 표현은 제한적으로만 사용해야 함 |
| 02_introduction | 서론 | raw_figure_filename | 초점을 맞추어, 내적 구성 타당도의 근거를 제시하고자 한다. 그림 1(fig01_framework.png)은 본 연구에서 제안하는 전체 분석 프레임워크를 도식화한 것이다. 이 | medium | 그림 파일명이 본문에 그대로 남아 있음 |
| 02_introduction | 서론 | raw_figure_filename | 된 표 (해당 목차에 포함된 표 없음) ## 이 목차에 포함된 이미지 fig01_framework.png | medium | 그림 파일명이 본문에 그대로 남아 있음 |
| 02_introduction | 서론 | diagnosis_claim | 일 지수로 통합하는 접근을 취한다. 이때 중요한 점은 본 연구의 결과를 임상적 스트레스 진단이나 지도학습 기반 예측모형으로 설명하지 않는다는 것이다. 대신, 제안된 | medium | 진단 표현은 제한적으로만 사용해야 함 |
| 03_related_work | 선행연구 | diagnosis_claim | 류하는 데 초점을 맞춘다. 그러나 이러한 접근법은 외부 준거 라벨(예: 임상적 스트레스 진단)이 없는 조건에서는 적용이 제한적이다. 본 연구는 지도학습 기반 예측모 | medium | 진단 표현은 제한적으로만 사용해야 함 |
| 04_data_preprocessing | 데이터 및 전처리 | raw_figure_filename | 관 후 폐기 예정이며, 분석 결과는 집계 수준에서만 보고된다. 그림 2(fig02_stress_distribution.png)는 전체 데이터의 스트레스 점수 분포를 시각화한 것으로, 스트레스 점수 | medium | 그림 파일명이 본문에 그대로 남아 있음 |
| 04_data_preprocessing | 데이터 및 전처리 | raw_figure_filename | feature_quality.csv ## 이 목차에 포함된 이미지 - fig02_stress_distribution.png | medium | 그림 파일명이 본문에 그대로 남아 있음 |
| 04_data_preprocessing | 데이터 및 전처리 | raw_csv_filename | 가정을 일부 충족할 가능성을 시사한다. ## 이 목차에 포함된 표 - descriptive_statistics.csv - stress_index_feature_quality.csv ## | medium | CSV 파일명이 본문에 그대로 남아 있음 |
| 04_data_preprocessing | 데이터 및 전처리 | raw_csv_filename | 차에 포함된 표 - descriptive_statistics.csv - stress_index_feature_quality.csv ## 이 목차에 포함된 이미지 - fig02_stress_distri | medium | CSV 파일명이 본문에 그대로 남아 있음 |
| 04_data_preprocessing | 데이터 및 전처리 | aihub_claim | 향 모델 사전 학습 및 특징 추출 파이프라인 검증에 활용되었다. 셋째, AIHub 보조 데이터는 다양한 화자와 발화 환경을 포함하여 음성 처리 알고리즘의 | medium | AIHub를 실제 검증 결과처럼 썼는지 확인 필요 |
| 05_stress_index_method | 스트레스 지수 설계 방법 | raw_figure_filename | 는 표본 내에서 스트레스 수준이 비교적 고르게 분포함을 시사한다. 그림 fig02_stress_distribution.png는 이 지수의 분포를 시각화하여 보여준다. 결론적으로, 본 연구에서 제 | medium | 그림 파일명이 본문에 그대로 남아 있음 |
| 05_stress_index_method | 스트레스 지수 설계 방법 | raw_figure_filename | tive_statistics.csv ## 이 목차에 포함된 이미지 - fig02_stress_distribution.png | medium | 그림 파일명이 본문에 그대로 남아 있음 |
| 05_stress_index_method | 스트레스 지수 설계 방법 | raw_csv_filename | 다만, 기술통계 결과 WPM 변수는 모든 관측치에서 0의 값을 보여(표 descriptive_statistics.csv 참조) 실제 지수 계산에서 기여도가 없었으나, 이론적 틀을 유지하기 위 | medium | CSV 파일명이 본문에 그대로 남아 있음 |
| 05_stress_index_method | 스트레스 지수 설계 방법 | raw_csv_filename | 는 평균 0.495, 표준편차 0.194, 범위 0–1로 나타났으며(표 descriptive_statistics.csv 참조), 이는 표본 내에서 스트레스 수준이 비교적 고르게 분포함을 시사 | medium | CSV 파일명이 본문에 그대로 남아 있음 |
| 05_stress_index_method | 스트레스 지수 설계 방법 | raw_csv_filename | 측모형으로 확대 해석되어서는 안 된다. ## 이 목차에 포함된 표 - descriptive_statistics.csv ## 이 목차에 포함된 이미지 - fig02_stress_distri | medium | CSV 파일명이 본문에 그대로 남아 있음 |
| 05_stress_index_method | 스트레스 지수 설계 방법 | diagnosis_claim | ernal construct validity) 근거로 해석되어야 하며, 임상적 스트레스 진단이나 지도학습 기반 예측모형으로 확대 해석되어서는 안 된다. ## 이 | medium | 진단 표현은 제한적으로만 사용해야 함 |
| 06_stt_reliability | STT 신뢰도 검증 | raw_csv_filename | 하는 지표로 활용된다는 점을 강조한다. ## 이 목차에 포함된 표 - stt_reliability.csv ## 이 목차에 포함된 이미지 - fig_stt_reliability | medium | CSV 파일명이 본문에 그대로 남아 있음 |
| 06_stt_reliability | STT 신뢰도 검증 | diagnosis_claim | 련된 음성 변화를 반영할 가능성을 시사한다. 다만, 본 연구의 WPM은 임상적 스트레스 진단이나 지도학습 기반 예측모형으로 사용되지 않으며, 오직 음성 특성의 상대 | medium | 진단 표현은 제한적으로만 사용해야 함 |
| 07_construct_validity | 구성 타당도 검증 결과 | raw_figure_filename | 68, -0.412]로 부적 상관의 방향성이 일관되게 유지되었다. 그림 fig04_bootstrap_ci.png는 각 변수의 Bootstrap 신뢰구간을 시각화한다. ## 3.3 가 | medium | 그림 파일명이 본문에 그대로 남아 있음 |
| 07_construct_validity | 구성 타당도 검증 결과 | raw_figure_filename | 화 지속 시간이 지수 구성에 중요한 기여를 하고 있음을 시사한다. 그림 fig05_weight_sensitivity.png는 각 시나리오별 상관계수 변화를 도시한다. ## 3.4 Leave-O | medium | 그림 파일명이 본문에 그대로 남아 있음 |
| 07_construct_validity | 구성 타당도 검증 결과 | raw_figure_filename | rgy_mean.png - scatter_pitch_mean.png - fig04_bootstrap_ci.png - fig05_weight_sensitivity.png - anova_ | medium | 그림 파일명이 본문에 그대로 남아 있음 |
| 07_construct_validity | 구성 타당도 검증 결과 | raw_figure_filename | tch_mean.png - fig04_bootstrap_ci.png - fig05_weight_sensitivity.png - anova_energy_mean.png - anova_pitch_m | medium | 그림 파일명이 본문에 그대로 남아 있음 |
| 07_construct_validity | 구성 타당도 검증 결과 | raw_figure_filename | 며, 기존 문헌에서 보고된 음성 스트레스 지표와 일관된 결과이다. 그림 scatter_energy_mean.png와 scatter_pitch_mean.png는 각 변수와 스트레스 점수 | medium | 그림 파일명이 본문에 그대로 남아 있음 |
| 07_construct_validity | 구성 타당도 검증 결과 | raw_figure_filename | 와 일관된 결과이다. 그림 scatter_energy_mean.png와 scatter_pitch_mean.png는 각 변수와 스트레스 점수 간의 산점도를 보여준다. 판별 타당도 측면 | medium | 그림 파일명이 본문에 그대로 남아 있음 |
| 07_construct_validity | 구성 타당도 검증 결과 | raw_figure_filename | alidity_summary.csv ## 이 목차에 포함된 이미지 - scatter_energy_mean.png - scatter_pitch_mean.png - fig04_bootst | medium | 그림 파일명이 본문에 그대로 남아 있음 |
| 07_construct_validity | 구성 타당도 검증 결과 | raw_figure_filename | 목차에 포함된 이미지 - scatter_energy_mean.png - scatter_pitch_mean.png - fig04_bootstrap_ci.png - fig05_weight | medium | 그림 파일명이 본문에 그대로 남아 있음 |
| 07_construct_validity | 구성 타당도 검증 결과 | raw_csv_filename | 준거를 활용한 추가 검증이 요구된다. ## 이 목차에 포함된 표 - correlation_validity.csv - bootstrap_ci.csv - weight_sensitivity | medium | CSV 파일명이 본문에 그대로 남아 있음 |
| 07_construct_validity | 구성 타당도 검증 결과 | raw_csv_filename | 목차에 포함된 표 - correlation_validity.csv - bootstrap_ci.csv - weight_sensitivity.csv - loo_stabilit | medium | CSV 파일명이 본문에 그대로 남아 있음 |
| 07_construct_validity | 구성 타당도 검증 결과 | raw_csv_filename | ation_validity.csv - bootstrap_ci.csv - weight_sensitivity.csv - loo_stability.csv - anova_profile.csv | medium | CSV 파일명이 본문에 그대로 남아 있음 |
| 07_construct_validity | 구성 타당도 검증 결과 | raw_csv_filename | strap_ci.csv - weight_sensitivity.csv - loo_stability.csv - anova_profile.csv - anova_group_descr | medium | CSV 파일명이 본문에 그대로 남아 있음 |
| 07_construct_validity | 구성 타당도 검증 결과 | raw_csv_filename | t_sensitivity.csv - loo_stability.csv - anova_profile.csv - anova_group_descriptive.csv - constru | medium | CSV 파일명이 본문에 그대로 남아 있음 |
| 07_construct_validity | 구성 타당도 검증 결과 | raw_csv_filename | loo_stability.csv - anova_profile.csv - anova_group_descriptive.csv - construct_validity_summary.csv ## 이 | medium | CSV 파일명이 본문에 그대로 남아 있음 |
| 07_construct_validity | 구성 타당도 검증 결과 | raw_csv_filename | ile.csv - anova_group_descriptive.csv - construct_validity_summary.csv ## 이 목차에 포함된 이미지 - scatter_energy_mean | medium | CSV 파일명이 본문에 그대로 남아 있음 |
| 07_construct_validity | 구성 타당도 검증 결과 | raw_missing_value | 보여준다. 판별 타당도 측면에서, 분당 음절 수(WPM)는 상관계수가 NaN으로 산출되어 분석에서 제외되었다. 이는 WPM이 본 연구의 스트레스 점 | high | 결측/None 표기가 본문에 남아 있음 |
| 08_robustness_sensitivity | 강건성 및 민감도 분석 | raw_csv_filename | 기준 지수와의 상관관계 및 평균 절대 차이(MAD)를 평가하였다. 표 `stt_error_sensitivity.csv`에 제시된 바와 같이, 모든 섭동 시나리오에서 기준 지수와의 상관계수는 | medium | CSV 파일명이 본문에 그대로 남아 있음 |
| 08_robustness_sensitivity | 강건성 및 민감도 분석 | raw_csv_filename | ch_gt_350_removed, n=100) 조건을 적용하였다. 표 `outlier_robustness.csv`에 제시된 바와 같이, 전체 데이터에서 에너지 평균과 피치 평균 간 상 | medium | CSV 파일명이 본문에 그대로 남아 있음 |
| 08_robustness_sensitivity | 강건성 및 민감도 분석 | raw_csv_filename | 상관 구조가 더 명확해짐을 시사한다. ## 이 목차에 포함된 표 - stt_error_sensitivity.csv - outlier_robustness.csv ## 이 목차에 포함된 | medium | CSV 파일명이 본문에 그대로 남아 있음 |
| 08_robustness_sensitivity | 강건성 및 민감도 분석 | raw_csv_filename | 목차에 포함된 표 - stt_error_sensitivity.csv - outlier_robustness.csv ## 이 목차에 포함된 이미지 - fig_stt_error_sensi | medium | CSV 파일명이 본문에 그대로 남아 있음 |
| 09_discussion | 논의 | raw_csv_filename | 접근법을 도입한 후속 연구가 요구된다. ## 이 목차에 포함된 표 - construct_validity_summary.csv ## 이 목차에 포함된 이미지 - (해당 목차에 포함된 이미지가 명시 | medium | CSV 파일명이 본문에 그대로 남아 있음 |
| 09_discussion | 논의 | diagnosis_claim | 다. 따라서 제안된 지수의 타당도는 내적 구성 타당도 근거에 국한되며, 임상적 스트레스 진단이나 지도학습 기반 예측모형으로 확장 해석되어서는 안 된다. 이 지수는 | medium | 진단 표현은 제한적으로만 사용해야 함 |
| 10_limitations_conclusion | 한계 및 결론 | placeholder_figure | 이 스트레스와 관련된 심리적 상태를 반영할 수 있음을 시사한다. 특히, [그림 X]에서 확인할 수 있듯이, 특정 음성 임베딩 차원과 텍스트 부정성 점수 간 | high | 그림 placeholder가 남아 있음 |
| 10_limitations_conclusion | 한계 및 결론 | unverified_embedding | 를 향상시키기 위한 후처리 과정을 도입해야 한다. 다섯째, 본 연구는 음성 임베딩(embeddings)과 텍스트 부정성 점수라는 두 가지 단일 모달리티에 | high | 실제 분석하지 않은 임베딩 표현 가능성 |
| 10_limitations_conclusion | 한계 및 결론 | unverified_embedding | 위한 후처리 과정을 도입해야 한다. 다섯째, 본 연구는 음성 임베딩(embeddings)과 텍스트 부정성 점수라는 두 가지 단일 모달리티에 초점을 맞추었다. | high | 실제 분석하지 않은 임베딩 표현 가능성 |
| 10_limitations_conclusion | 한계 및 결론 | unverified_embedding | 수 있음을 시사한다. 특히, [그림 X]에서 확인할 수 있듯이, 특정 음성 임베딩 차원과 텍스트 부정성 점수 간의 상관 패턴은 이론적으로 예측된 방향성과 | high | 실제 분석하지 않은 임베딩 표현 가능성 |
| 10_limitations_conclusion | 한계 및 결론 | unverified_text_negative | 비표준 발음에서 STT 오류율이 높아질 경우, 텍스트 기반 부정성 점수(text negativity scores)의 신뢰성이 저하될 수 있다. 또한, 사용된 오디오 코덱의 | high | 실제 분석하지 않은 텍스트 부정성 표현 가능성 |
| 10_limitations_conclusion | 한계 및 결론 | unverified_text_negative | 입해야 한다. 다섯째, 본 연구는 음성 임베딩(embeddings)과 텍스트 부정성 점수라는 두 가지 단일 모달리티에 초점을 맞추었다. 스트레스는 음성, | high | 실제 분석하지 않은 텍스트 부정성 표현 가능성 |
| 10_limitations_conclusion | 한계 및 결론 | unverified_text_negative | . 특히, [그림 X]에서 확인할 수 있듯이, 특정 음성 임베딩 차원과 텍스트 부정성 점수 간의 상관 패턴은 이론적으로 예측된 방향성과 일치하였다. 결론적으 | high | 실제 분석하지 않은 텍스트 부정성 표현 가능성 |
| 10_limitations_conclusion | 한계 및 결론 | diagnosis_claim | 몇 가지 중요한 한계를 지닌다. 첫째, 본 연구는 외부 준거 라벨(예: 임상적 스트레스 진단, 생리학적 측정치)이 부재한 상태에서 수행되었다. 따라서 제안된 지수들 | medium | 진단 표현은 제한적으로만 사용해야 함 |
산출물 텍스트: results/manuscript_text/data_quality_action_log_ko.md
# 데이터 품질 자동 조치 계획 ## 1. VOC STT/WPM 자동 복구 - 코드: `FIX_VOC_STT_WPM` - 원인: 전사문, 단어 수, WPM 또는 z_wpm이 비정상입니다. VOC STT부터 다시 돌려야 합니다. - 트리거: VOC transcript column; VOC word_cnt; VOC text_len; VOC wpm; Stress index z_wpm - 자동 실행 가능: YES - 실행 힌트: `python src/auto_remediate.py --fix-voc-stt-wpm --model medium --limit <N> --overwrite-stt` ## 3. 상수/결측 변수 해석 제한 표시 - 코드: `REVIEW_FEATURE_QUALITY` - 원인: 상수 또는 결측 처리된 변수가 있습니다. 자동 복구보다 STT/원본 데이터 확인이 우선입니다. - 트리거: Feature quality table - 자동 실행 가능: PARTIAL - 실행 힌트: `먼저 FIX_VOC_STT_WPM 실행. 이후에도 남으면 해당 변수는 본문에서 보조/제외로 표시` ## 4. 표본 수 확대 재실행 - 코드: `RUN_LARGER_SAMPLE` - 원인: 현재는 테스트/파일럿 표본입니다. SCI 원고용은 500개 이상, 가능하면 전체/998건으로 재실행해야 합니다. - 트리거: VOC feature sample size - 자동 실행 가능: YES - 실행 힌트: `대시보드 테스트 개수 500/1000/전체 선택 후 전체 파이프라인 또는 자동 조치 실행`
산출물 텍스트: results/manuscript_text/data_quality_warning_ko.md
# 데이터 품질 점검 결과 - PASS: 1 - WARN: 3 - FAIL: 5 - 논문용 사용 가능 여부: 불가 또는 추가 확인 필요 ## 조치 필요 항목 - **VOC Whisper STT result file** [WARN]: VOC STT 파일은 있으나 빈 전사문 비율이 높습니다. → 빈 전사문이 많으면 Whisper model/device 설정, 음성 변환 파일, VAD 설정을 확인하세요. - **VOC feature sample size** [WARN]: 현재 VOC 특징 데이터는 n=100입니다. → SCI 최종 원고 전에는 500개 이상, 가능하면 전체/998건으로 재실행하세요. - **VOC transcript column** [FAIL]: transcript가 대부분 비어 있습니다. 현재 결과는 STT 제외 테스트 결과입니다. → STT 제외 체크를 해제하고 VOC Whisper STT 포함으로 전체 파이프라인을 재실행하세요. - **VOC word_cnt** [FAIL]: word_cnt 산출 상태 확인 → FAIL이면 STT 포함 재실행 필요 - **VOC text_len** [FAIL]: text_len 산출 상태 확인 → FAIL이면 STT 포함 재실행 필요 - **VOC wpm** [FAIL]: wpm이 전부 0 또는 상수입니다. 논문용 stress_score에 사용할 수 없습니다. → STT 제외 체크 해제 → VOC Whisper STT 실행 → VOC 특징 추출 → Stress Index 재산출 순서로 다시 실행하세요. - **Stress index z_wpm** [FAIL]: z_wpm이 전부 0입니다. WPM이 지수에 반영되지 않았습니다. → VOC STT 포함 재실행 후 stress_score를 재산출하세요. - **Feature quality table** [WARN]: 상수/결측 처리된 변수가 있습니다. → 해당 변수가 후속 원고에서 현재 분석 결과처럼 해석되지 않도록 주의하세요. ## 실행 권장 순서 1. Whisper STT 메뉴에서 dataset=voc, model=medium으로 STT 실행 2. VOC 특징 추출 실행 3. Stress Index 재산출 실행 4. 데이터 품질 점검 실행 5. PASS 확인 후 구성 타당도 분석과 논문 원고 생성 진행
산출물 텍스트: results/manuscript_text/deploy_result_integrity_ko.md
# 모바일 배포 결과값 출처 점검 - 생성시각: 2026-07-04 18:35:46 - 프로파일: `n1000` - 판정: **READY** - 메시지: 모바일 배포에 사용할 Feature/Stress 결과가 확인됐습니다. 0건 표시가 나오면 패키지가 오래된 것입니다. ## 핵심 사용 출처 - Features: 1000건 · root · `C:\AI\sci_voc_bot\data\voc\features\voc_features.csv` - Stress: 1000건 · root · `C:\AI\sci_voc_bot\results\stress_index\voc_stress_score_rebuilt.csv` - STT small: 1000건 · root · `C:\AI\sci_voc_bot\data\voc\stt\voc_whisper_small_results.csv` ## 해석 모바일 콘솔은 분석을 실행하는 곳이 아니라, 로컬에서 생성된 결과값을 확인하는 곳입니다. 따라서 0건으로 보이면 먼저 결과 파일 부재가 아니라 잘못된 snapshot 또는 오래된 배포 패키지를 의심해야 합니다.
결과 해석
건수의 단위를 구분하면 57,619 세그먼트와 2,000 슬롯을 2,000개의 새 음성 파일로 오해하는 문제를 방지할 수 있다.
한계 및 논문 반영 기준
2,000은 원본 음성 건수가 아니라 1,000통화×2화자 후보 슬롯이다.
5. AS-IS/TO-BE 비교 및 추가 분석
논문 작성용 상세 본문
AS-IS는 최종 논문 본문 타당도 분석이고, TO-BE는 자동화 재현성 검증이며, 화자분리 확장은 통화 내부 상호작용 구조를 탐색하는 분석이다.
세 축은 같은 데이터를 일부 공유하지만 연구 질문과 분석 단위가 다르므로 하나의 결과표에서 단순히 동일 기준으로 비교해서는 안 된다.
추가 분석은 발화량·턴·응답쌍과 같은 구조적 지표부터 제시하고, segment-level stress가 준비된 뒤 스트레스 연관성 분석을 확장하는 순서가 안전하다.
AS-IS와 TO-BE의 수치 차이는 동일 조건에서의 성능 차이로 바로 해석할 수 없다. 표본 구성, STT 버전, WPM 산출, 전처리, 스키마와 결과 슬롯을 먼저 확인해야 한다.
추가 분석은 현재 확보된 발화량·턴·응답쌍부터 시작하고, segment-level STT와 WPM이 완성된 뒤 stress 연관성으로 확장해야 한다.
이 단계적 접근은 후속 결과가 본문 분석의 신뢰성을 침해하지 않도록 하는 연구 연속성 전략이다.
분석 수치 및 결과표
표 5-1. 분석 축 비교
| 분석 축 | 단위 | 핵심 질문 | 현재 상태 |
|---|---|---|---|
| AS-IS | 통화 전체 | 지수 구성 타당도 | 완료 |
| TO-BE | 통화 전체 | 자동화 재현성 | 완료 |
| Speaker extension | 세그먼트/슬롯 | 화자 후보 상호작용 | 부분 완료 |
| Segment stress | 세그먼트 | 시간 순서 stress 연관 | PENDING |
표 5-2. 추가 분석 우선순위
| 우선순위 | 분석 | 필요 데이터 |
|---|---|---|
| 1 | 발화량/턴/응답쌍 | 현재 가능 |
| 2 | 세그먼트 STT/WPM | 추가 병합 |
| 3 | segment stress_score | STT/WPM 완료 후 |
| 4 | 역할 기반 고객/상담사 분석 | role mapping 검증 후 |
표 5-Q. 연구 질문
| 번호 | 연구 질문 |
|---|---|
| 1 | AS-IS와 TO-BE의 차이는 성능 차이인가, 파이프라인·표본·스키마 차이인가? |
| 2 | 추가 분석은 어떤 순서로 진행해야 하는가? |
표 5-M. 분석 방법 및 도구
| 순서 | 방법/도구 |
|---|---|
| 1 | Lineage comparison |
| 2 | Schema comparison |
| 3 | Sample-condition control |
| 4 | Additional-analysis prioritization |
분석 그림 및 도식
원본 분석 산출물 연계
산출물 CSV: research_continuity/11_asis_tobe_gap_v163/asis_tobe_gap_causes_v163.csv (행 5, 열 6)
전체 행 포함
| rank | cause | evidence | expected_effect | mp3_only | fix |
|---|---|---|---|---|---|
| 1 | 정규화 기준 차이 | 0413 AS-IS는 GLOBAL_STATS와 RAW_MIN/RAW_MAX를 고정해 stress_score를 계산한다. TO-BE가 현재 배치 기준 평균·표준편차 또는 새 min/max를 쓰면 평균이 이동한다. | 평균 stress_score 전체 이동 가능 | 아님 | TO-BE 검증용 AS-IS-compatible stress_score를 별도 산출한다. 0413 GLOBAL_STATS, ASIS_RAW_MIN, ASIS_RAW_MAX를 그대로 쓴다. |
| 2 | 음성 특징 추출 방식 차이 | 0406은 librosa.feature.rms(frame 기반)와 C2~C7 pitch를 사용했고, 0413은 sqrt(mean(y^2))와 fmin=50/fmax=600을 사용한다. TO-BE가 어느 쪽을 쓰는지에 따라 energy/pitch 평균이 달라진다. | energy_mean, pitch_mean 이동 → stress_score 평균 이동 | 부분 가능하지만 단독 원인으로 단정 금지 | 동일 wav_id 교집합에서 0413 방식으로 재추출한 compatible feature를 만든다. |
| 3 | STT/WPM 계산 차이 | wpm은 word_cnt와 duration_sec로 재계산된다. STT 파일, word count 규칙, duration 측정이 달라지면 stress_score가 바뀐다. | wpm z-score 이동 → 평균과 상관 일부 이동 | 아님 | AS-IS STT 파일 또는 같은 word_cnt 기준으로 재계산한 WPM을 비교표에 함께 표시한다. |
| 4 | 표본 수/제외 기준 차이 | AS-IS 계열은 n=998 기준이고 TO-BE는 raw n1000 및 analysis-ready n992를 분리한다. 같은 평균 비교가 아니다. | 결측/이상치 2~8건만으로도 평균과 분산이 일부 이동 가능 | 아님 | same-wav_id intersection 비교를 우선 표시하고, raw/analysis-ready는 분리 표시한다. |
| 5 | MP3/WAV 변환 차이 | 원본 압축/복원, ffmpeg 옵션, 샘플레이트, mono 변환이 다르면 energy와 duration이 변할 수 있다. | 주로 energy/duration에 영향. 그러나 0.118584 평균 차이를 단독으로 설명한다고 단정하기 어렵다. | 가능성은 있으나 검증 전 OK 처리 금지 | 동일 파일명 hash/ffprobe duration/samplerate/codec 비교를 통과한 뒤에만 '단순 음원 변환 차이'로 분류한다. |
산출물 JSON: research_continuity/11_asis_tobe_gap_v163/asis_tobe_gap_diagnosis_v163.json
전체 행 포함
| 경로 | 값 |
|---|---|
| version | v163_asis_tobe_gap_diagnosis |
| state | GAP_DIAGNOSIS_REQUIRED__NOT_MP3_ONLY_YET |
| as_is_stress_score_mean | 0.517904 |
| to_be_stress_score_mean | 0.636488 |
| difference | 0.118584 |
| absolute_difference | 0.118584 |
| gap_level | LARGE_FOR_EXACT_REPRODUCTION |
| mp3_only_judgement | 아직 단순 MP3/WAV 차이라고 단정하면 안 됩니다. 같은 wav_id·같은 산식·같은 정규화로 재계산한 뒤 남는 차이만 음원 변환 차이로 볼 수 있습니다. |
| safe_interpretation | 현재 차이는 평균값 완전재현이 아니라 방향성·상관 부호·해석 구조 유사도 검증으로 설명해야 합니다. |
| target_policy | 본문은 0413 AS-IS 유지. TO-BE 원본값은 보존. 추가로 AS-IS-compatible TO-BE 값을 만들어 차이를 줄일 수 있는지 감사표에서 확인. |
| asis_global_stats_locked.energy_mean.mu | 0.048839 |
| asis_global_stats_locked.energy_mean.sigma | 0.02286 |
| asis_global_stats_locked.energy_mean.min | 0.00854 |
| asis_global_stats_locked.energy_mean.max | 0.176261 |
| asis_global_stats_locked.pitch_mean.mu | 234.5089 |
| asis_global_stats_locked.pitch_mean.sigma | 35.5084 |
| asis_global_stats_locked.pitch_mean.min | 118.8143 |
| asis_global_stats_locked.pitch_mean.max | 568.1591 |
| asis_global_stats_locked.wpm.mu | 83.6358 |
| asis_global_stats_locked.wpm.sigma | 19.0283 |
| asis_global_stats_locked.wpm.min | 3.1496 |
| asis_global_stats_locked.wpm.max | 134.2513 |
| asis_global_stats_locked.duration_sec.mu | 146.7202 |
| asis_global_stats_locked.duration_sec.sigma | 170.316 |
| asis_global_stats_locked.duration_sec.min | 2.04 |
| asis_global_stats_locked.duration_sec.max | 1719.3 |
| asis_raw_min | -4.621053665640707 |
| asis_raw_max | 4.61997054885815 |
| likely_causes[0].rank | 1 |
| likely_causes[0].cause | 정규화 기준 차이 |
| likely_causes[0].evidence | 0413 AS-IS는 GLOBAL_STATS와 RAW_MIN/RAW_MAX를 고정해 stress_score를 계산한다. TO-BE가 현재 배치 기준 평균·표준편차 또는 새 min/max를 쓰면 평균이 이동한다. |
| likely_causes[0].expected_effect | 평균 stress_score 전체 이동 가능 |
| likely_causes[0].mp3_only | 아님 |
| likely_causes[0].fix | TO-BE 검증용 AS-IS-compatible stress_score를 별도 산출한다. 0413 GLOBAL_STATS, ASIS_RAW_MIN, ASIS_RAW_MAX를 그대로 쓴다. |
| likely_causes[1].rank | 2 |
| likely_causes[1].cause | 음성 특징 추출 방식 차이 |
| likely_causes[1].evidence | 0406은 librosa.feature.rms(frame 기반)와 C2~C7 pitch를 사용했고, 0413은 sqrt(mean(y^2))와 fmin=50/fmax=600을 사용한다. TO-BE가 어느 쪽을 쓰는지에 따라 energy/pitch 평균이 달라진다. |
| likely_causes[1].expected_effect | energy_mean, pitch_mean 이동 → stress_score 평균 이동 |
| likely_causes[1].mp3_only | 부분 가능하지만 단독 원인으로 단정 금지 |
| likely_causes[1].fix | 동일 wav_id 교집합에서 0413 방식으로 재추출한 compatible feature를 만든다. |
| likely_causes[2].rank | 3 |
| likely_causes[2].cause | STT/WPM 계산 차이 |
| likely_causes[2].evidence | wpm은 word_cnt와 duration_sec로 재계산된다. STT 파일, word count 규칙, duration 측정이 달라지면 stress_score가 바뀐다. |
| likely_causes[2].expected_effect | wpm z-score 이동 → 평균과 상관 일부 이동 |
| likely_causes[2].mp3_only | 아님 |
| likely_causes[2].fix | AS-IS STT 파일 또는 같은 word_cnt 기준으로 재계산한 WPM을 비교표에 함께 표시한다. |
| likely_causes[3].rank | 4 |
| likely_causes[3].cause | 표본 수/제외 기준 차이 |
| likely_causes[3].evidence | AS-IS 계열은 n=998 기준이고 TO-BE는 raw n1000 및 analysis-ready n992를 분리한다. 같은 평균 비교가 아니다. |
| likely_causes[3].expected_effect | 결측/이상치 2~8건만으로도 평균과 분산이 일부 이동 가능 |
| likely_causes[3].mp3_only | 아님 |
| likely_causes[3].fix | same-wav_id intersection 비교를 우선 표시하고, raw/analysis-ready는 분리 표시한다. |
| likely_causes[4].rank | 5 |
| likely_causes[4].cause | MP3/WAV 변환 차이 |
| likely_causes[4].evidence | 원본 압축/복원, ffmpeg 옵션, 샘플레이트, mono 변환이 다르면 energy와 duration이 변할 수 있다. |
| likely_causes[4].expected_effect | 주로 energy/duration에 영향. 그러나 0.118584 평균 차이를 단독으로 설명한다고 단정하기 어렵다. |
| likely_causes[4].mp3_only | 가능성은 있으나 검증 전 OK 처리 금지 |
| likely_causes[4].fix | 동일 파일명 hash/ffprobe duration/samplerate/codec 비교를 통과한 뒤에만 '단순 음원 변환 차이'로 분류한다. |
| oneclick_actions_v163[0].action | A |
| oneclick_actions_v163[0].name | AS-IS-compatible TO-BE 평균 추가 |
| oneclick_actions_v163[0].detail | TO-BE 원본값은 유지하고, 0413 고정 산식으로 다시 계산한 tobe_asis_compatible_stress_mean을 별도 산출한다. |
| oneclick_actions_v163[1].action | B |
| oneclick_actions_v163[1].name | Gap Decomposition 표 생성 |
| oneclick_actions_v163[1].detail | 정규화/feature/STT-WPM/표본/음원 변환을 분리해 차이 원인을 표로 표시한다. |
| oneclick_actions_v163[2].action | C |
| oneclick_actions_v163[2].name | same-id 교집합 비교 |
| oneclick_actions_v163[2].detail | AS-IS와 TO-BE에 모두 있는 wav_id만 비교해 mp3·표본 차이를 제거한 순수 산식 차이를 본다. |
| oneclick_actions_v163[3].action | D |
| oneclick_actions_v163[3].name | 교수님 문장 고정 |
| oneclick_actions_v163[3].detail | TO-BE가 AS-IS를 완전 재현했다는 표현은 금지하고, 방향성 유사도 및 재현성 보조근거로 설명한다. |
| existing_evidence_scan.candidate_files_scanned | 2 |
| existing_evidence_scan.evidence_hits[0].relative_path | research_continuity/05_oneclick_flow/latest_flow_status_v162.json |
| existing_evidence_scan.evidence_hits[0].asis_detected | |
| existing_evidence_scan.evidence_hits[0].tobe_detected | |
| existing_evidence_scan.evidence_hits[0].contains_stress_score_mean | True |
| existing_evidence_scan.evidence_hits[1].relative_path | research_continuity/05_oneclick_flow/latest_flow_status_v161.json |
| existing_evidence_scan.evidence_hits[1].asis_detected | |
| existing_evidence_scan.evidence_hits[1].tobe_detected | |
| existing_evidence_scan.evidence_hits[1].contains_stress_score_mean | True |
| professor_sentence | AS-IS와 TO-BE의 stress_score 평균 차이가 남아 있어 TO-BE가 AS-IS를 완전 재현했다고 보지는 않았습니다. 다만 동일 wav_id 교집합, 0413 고정 산식, 동일 WPM 기준으로 AS-IS-compatible 값을 별도 산출해 차이 원인을 정규화·특징추출·STT/WPM·표본 차이로 분해하겠습니다. |
산출물 CSV: research_continuity/15_asis_tobe_warn_source_fix_v169/csv_schema_compare_resolved_v169.csv (행 138, 열 17)
전체 행 포함
| pair_key | filename | as_is_path | as_is_rows | as_is_cols | as_is_columns | as_is_status | to_be_path | to_be_rows | to_be_cols | to_be_columns | to_be_status | status | original_status | professor_ready_status | resolution_status | resolution_note |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| anova.csv | anova.csv | 05_results\anova.csv | 4.0 | 4.0 | variable; F; p_value; eta_squared | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| correlation_4vars.csv | correlation_4vars.csv | 05_results\correlation_4vars.csv | 4.0 | 5.0 | metric; x; value; p_value; n | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| datasets0413_clean.csv | datasets0413_clean.csv | 04_final\datasets0413_clean.csv | 998.0 | 13.0 | wav_id; energy_mean; pitch_mean; duration_sec; word_cnt; wpm; text_len; z_energy_mean; z_pitch_mean; z_wpm; z_duration_sec; raw_stress; stress_score | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| datasets0413_clean_rms.csv | datasets0413_clean_rms.csv | 04_final\datasets0413_clean_rms.csv | 998.0 | 13.0 | wav_id; energy_mean; pitch_mean; duration_sec; word_cnt; text_len; wpm; z_energy_mean; z_pitch_mean; z_wpm; z_duration_sec; raw_stress; stress_score | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| descriptive_stats.csv | descriptive_stats.csv | 05_results\descriptive_stats.csv | 7.0 | 9.0 | Unnamed: 0; count; mean; std; min; 25%; 50%; 75%; max | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| features_voc_0413.csv | features_voc_0413.csv | 03_features\features_voc_0413.csv | 1000.0 | 4.0 | wav_id; energy_mean; pitch_mean; duration_sec | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| features_voc_0413_rms.csv | features_voc_0413_rms.csv | 03_features\features_voc_0413_rms.csv | 1000.0 | 4.0 | wav_id; energy_mean; pitch_mean; duration_sec | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| global_ref_rms.csv | global_ref_rms.csv | 05_results\global_ref_rms.csv | 4.0 | 7.0 | variable; mu; sigma; min; max; method; source | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| leave_one_out.csv | leave_one_out.csv | 05_results\leave_one_out.csv | 4.0 | 4.0 | dropped_variable; corr_with_full; p_value; mean_abs_diff | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| sensitivity.csv | sensitivity.csv | 05_results\sensitivity.csv | 7.0 | 4.0 | scenario; corr_with_base; p_value; mean_abs_diff | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| stt_1000.csv | stt_1000.csv | stt_1000.csv | 1000.0 | 7.0 | wav_id; text; word_cnt; text_len; source; stt_ok; sample_order | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| tukey.csv | tukey.csv | 05_results\tukey.csv | 12.0 | 8.0 | variable; group1; group2; meandiff; p_adj; lower; upper; reject | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| validity_summary.csv | validity_summary.csv | 05_results\validity_summary.csv | 3.0 | 2.0 | item; value | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| discriminant_details.csv | discriminant_details.csv | 05_results\discriminant_details.csv | 4.0 | 4.0 | variable; r; p_value; abs_r | ok | results_by_count/n1000/snapshot/results\tables\discriminant_details.csv | 4.0 | 4.0 | variable; r; p_value; abs_r | ok | MATCH | MATCH | MATCH | NOT_APPLICABLE | |
| discriminant_details.csv | discriminant_details.csv | 05_results\discriminant_details.csv | 4.0 | 4.0 | variable; r; p_value; abs_r | ok | results_by_count/n1000/snapshot/results\validity\discriminant_details.csv | 4.0 | 4.0 | variable; r; p_value; abs_r | ok | MATCH | MATCH | MATCH | NOT_APPLICABLE | |
| bootstrap_ci.csv | bootstrap_ci.csv | 05_results\bootstrap_ci.csv | 4.0 | 5.0 | variable; boot_mean_r; ci_low_2_5; ci_high_97_5; n_boot | ok | results_by_count/n1000/snapshot/results\tables\bootstrap_ci.csv | 4.0 | 5.0 | variable; r; ci_lower; ci_upper; n_boot | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |
| bootstrap_ci.csv | bootstrap_ci.csv | 05_results\bootstrap_ci.csv | 4.0 | 5.0 | variable; boot_mean_r; ci_low_2_5; ci_high_97_5; n_boot | ok | results_by_count/n1000/snapshot/results\validity\bootstrap_ci.csv | 4.0 | 5.0 | variable; r; ci_lower; ci_upper; n_boot | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |
| correlation_matrix.csv | correlation_matrix.csv | 05_results\correlation_matrix.csv | 7.0 | 8.0 | Unnamed: 0; energy_mean; pitch_mean; wpm; duration_sec; stress_score; word_cnt; text_len | ok | results_by_count/n1000/snapshot/results\tables\correlation_matrix.csv | 7.0 | 8.0 | variable; stress_score; energy_mean; pitch_mean; wpm; duration_sec; word_cnt; text_len | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |
| correlation_matrix.csv | correlation_matrix.csv | 05_results\correlation_matrix.csv | 7.0 | 8.0 | Unnamed: 0; energy_mean; pitch_mean; wpm; duration_sec; stress_score; word_cnt; text_len | ok | results_by_count/n1000/snapshot/results\validity\correlation_matrix.csv | 7.0 | 8.0 | variable; stress_score; energy_mean; pitch_mean; wpm; duration_sec; word_cnt; text_len | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |
| multivariate_ols.csv | multivariate_ols.csv | 05_results\multivariate_ols.csv | 5.0 | 8.0 | term; coef; std_err; t; p_value; r2; adj_r2; n | ok | results_by_count/n1000/snapshot/results\tables\multivariate_ols.csv | 4.0 | 9.0 | term; coef; std_err; t; p_value; r2; adj_r2; n; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |
| multivariate_ols.csv | multivariate_ols.csv | 05_results\multivariate_ols.csv | 5.0 | 8.0 | term; coef; std_err; t; p_value; r2; adj_r2; n | ok | results_by_count/n1000/snapshot/results\validity\multivariate_ols.csv | 4.0 | 9.0 | term; coef; std_err; t; p_value; r2; adj_r2; n; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |
| univariate_ols.csv | univariate_ols.csv | 05_results\univariate_ols.csv | 4.0 | 7.0 | model; intercept; slope; r2; t_slope; p_slope; n | ok | results_by_count/n1000/snapshot/results\tables\univariate_ols.csv | 4.0 | 8.0 | model; intercept; slope; r2; t_slope; p_slope; n; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |
| univariate_ols.csv | univariate_ols.csv | 05_results\univariate_ols.csv | 4.0 | 7.0 | model; intercept; slope; r2; t_slope; p_slope; n | ok | results_by_count/n1000/snapshot/results\validity\univariate_ols.csv | 4.0 | 8.0 | model; intercept; slope; r2; t_slope; p_slope; n; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |
| vif.csv | vif.csv | 05_results\vif.csv | 4.0 | 2.0 | variable; VIF | ok | results_by_count/n1000/snapshot/results\tables\vif.csv | 4.0 | 3.0 | variable; VIF; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |
| vif.csv | vif.csv | 05_results\vif.csv | 4.0 | 2.0 | variable; VIF | ok | results_by_count/n1000/snapshot/results\validity\vif.csv | 4.0 | 3.0 | variable; VIF; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |
| additional_research_execution_order.csv | results_by_count/n1000/snapshot/results\tables\future_analysis\additional_research_execution_order.csv | 6.0 | 5.0 | order; step; menu; action; required_now | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| additional_research_plan.csv | results_by_count/n1000/snapshot/results\tables\future_analysis\additional_research_plan.csv | 5.0 | 9.0 | stage; topic; current_limitation; required_data; recommended_sample; method; expected_output; paper_usage; priority | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| additional_research_required_data.csv | results_by_count/n1000/snapshot/results\tables\future_analysis\additional_research_required_data.csv | 5.0 | 4.0 | data_item; columns; needed_for; current_template | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| additional_research_status.csv | results_by_count/n1000/snapshot/results\tables\future_analysis\additional_research_status.csv | 1.0 | 5.0 | task; status; stress_score_n; stt_reliability_available; message | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| additional_research_why_method_direction.csv | results_by_count/n1000/snapshot/results\tables\meeting_0627\additional_research_why_method_direction.csv | 5.0 | 5.0 | 주제; 왜 필요한가; 왜 이 기법인가; 방향성; 이번 보고에서의 표현 | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| aiis_branch_overview.csv | results_by_count/n1000/snapshot/results\tables\aiis_branch_overview.csv | 2.0 | 5.0 | branch_name; branch_dir; manifest_exists; status_exists; updated_at | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| aiis_final_diff_actions.csv | results_by_count/n1000/snapshot/results\tables\aiis_final_diff_actions.csv | 2.0 | 4.0 | priority; action; command_or_screen; done_when | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| aiis_final_diff_metrics.csv | results_by_count/n1000/snapshot/results\tables\aiis_final_diff_metrics.csv | 10.0 | 8.0 | metric; asis_value; tobe_value; delta; unit; status; reason; source | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| aiis_final_diff_overview.csv | results_by_count/n1000/snapshot/results\tables\aiis_final_diff_overview.csv | 9.0 | 3.0 | item; value; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| aiis_final_diff_root_causes.csv | results_by_count/n1000/snapshot/results\tables\aiis_final_diff_root_causes.csv | 5.0 | 7.0 | priority; level; title; evidence; why_it_happened; paper_impact; next_action | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| aiis_key_metric_compare.csv | results_by_count/n1000/snapshot/results\tables\aiis_key_metric_compare.csv | 6.0 | 8.0 | metric; asis_value; tobe_value; delta; asis_source; tobe_source; status; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| aiis_recommended_actions.csv | results_by_count/n1000/snapshot/results\tables\aiis_recommended_actions.csv | 1.0 | 7.0 | order; action_code; action_name; purpose; command_hint; dashboard_path; expected_result | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| aiis_root_causes.csv | results_by_count/n1000/snapshot/results\tables\aiis_root_causes.csv | 1.0 | 10.0 | cause_code; severity; title; problem; why; evidence; paper_impact; check_next; recommended_action_ref; source | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| aiis_snapshot_status.csv | results_by_count/n1000/snapshot/results\tables\aiis_snapshot_status.csv | 1.0 | 12.0 | slot; slot_snapshot_found; tobe_scan_roots; aiis_extension_status; core_message; asis_stt_n; tobe_stt_n; asis_feature_n; tobe_feature_n; asis_stress_n; tobe_stress_n; valid_tobe_stt_source | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| aiis_tracking_metrics.csv | results_by_count/n1000/snapshot/results\tables\aiis_tracking_metrics.csv | 4.0 | 5.0 | tracking_metric; target; current_value; status; source | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| anova_group_descriptive.csv | results_by_count/n1000/snapshot/results\tables\anova_group_descriptive.csv | 3.0 | 16.0 | stress_group; stress_score_count; stress_score_mean; stress_score_std; energy_mean_count; energy_mean_mean; energy_mean_std; pitch_mean_count; pitch_mean_mean; pitch_mean_std; wpm_count; wpm_mean; wpm_std; duration_sec_count; duration_sec_mean; duration_sec_std | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| anova_profile.csv | results_by_count/n1000/snapshot/results\tables\anova_profile.csv | 4.0 | 7.0 | variable; F; p_value; eta_squared; effect_size; note; p_value_display | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| anova_profile.csv | results_by_count/n1000/snapshot/results\validity\anova_profile.csv | 4.0 | 7.0 | variable; F; p_value; eta_squared; effect_size; note; p_value_display | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| applied_sciences_required_sections.csv | results_by_count/n1000/snapshot/results\tables\applied_sciences_required_sections.csv | 20.0 | 3.0 | section_or_statement; source_or_field; status | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| artifact_lineage.csv | results_by_count/n1000/snapshot/results\tables\repro\artifact_lineage.csv | 9.0 | 8.0 | artifact; depends_on; source_stage; upstream_1; upstream_2; upstream_3; upstream_4; risk_if_changed | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| auto_execution_plan.csv | results_by_count/n1000/snapshot/results\tables\repro\auto_execution_plan.csv | 2.0 | 10.0 | run_order; issue; status; severity; evidence; likely_cause; recommended_action; menu_no; command_hint; priority | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| claim_strength_check.csv | results_by_count/n1000/snapshot/results\tables\ops\claim_strength_check.csv | 10.0 | 5.0 | risk_phrase; recommended_expression; found_count; files; decision | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| complete_report_checklist.csv | results_by_count/n1000/snapshot/results\tables\complete_report_checklist.csv | 34.0 | 10.0 | order; group; title; kind; required; exists; rows_or_note; path; status; description | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| construct_validity_summary.csv | results_by_count/n1000/snapshot/results\tables\construct_validity_summary.csv | 7.0 | 3.0 | indicator; value; decision | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| correlation_validity.csv | results_by_count/n1000/snapshot/results\tables\correlation_validity.csv | 4.0 | 6.0 | variable; r; p_value; validity_type; decision; p_value_display | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| criterion_validity_status.csv | results_by_count/n1000/snapshot/results\tables\future_analysis\criterion_validity_status.csv | 1.0 | 4.0 | task; status; message; required_file | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| csv_profile_all.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\csv_profile_all.csv | 144.0 | 9.0 | side; relative_path; filename; stem; rows; cols; columns; status; scan_scope | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| csv_schema_compare.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\csv_schema_compare.csv | 138.0 | 13.0 | pair_key; filename; as_is_path; as_is_rows; as_is_cols; as_is_columns; as_is_status; to_be_path; to_be_rows; to_be_cols; to_be_columns; to_be_status; status | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| data_quality_action_plan.csv | results_by_count/n1000/snapshot/results\tables\data_quality_action_plan.csv | 3.0 | 7.0 | priority; action_code; action_name; trigger_items; reason; auto_executable; command_hint | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| data_quality_status.csv | results_by_count/n1000/snapshot/results\tables\data_quality_status.csv | 9.0 | 7.0 | check_item; status; value; criterion; message; recommended_action; severity | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| deepseek_learning_feedback.csv | results_by_count/n1000/snapshot/results\tables\learning\deepseek_learning_feedback.csv | 5.0 | 3.0 | area; status; recommendation | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| deploy_result_integrity.csv | results_by_count/n1000/snapshot/results\tables\deploy_result_integrity.csv | 22.0 | 7.0 | artifact; source_type; path; exists; rows_or_exists; mtime; size_kb | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| descriptive_statistics.csv | results_by_count/n1000/snapshot/results\tables\descriptive_statistics.csv | 5.0 | 9.0 | variable; count; mean; std; min; 25%; 50%; 75%; max | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| diagnosis.csv | results_by_count/n1000/snapshot/results\tables\repro\diagnosis.csv | 4.0 | 8.0 | issue; status; severity; evidence; likely_cause; recommended_action; menu_no; command_hint | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| difference_action_plan.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\difference_action_plan.csv | 2.0 | 5.0 | priority; action; why; evidence; target_menu | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| difference_problem_trace.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\difference_problem_trace.csv | 19.0 | 6.0 | metric_key; status; as_is_value; to_be_value; as_is_source; to_be_source | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| difference_root_cause_analysis.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\difference_root_cause_analysis.csv | 2.0 | 8.0 | priority; domain; severity; finding; evidence; likely_cause; next_check; recommended_action | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| failed_file_inventory.csv | results_by_count/n1000/snapshot/results\tables\ops\failed_file_inventory.csv | 600.0 | 9.0 | profile; dataset; source_csv; file_id; file; status; error; ok; transcript_len | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| feature_descriptive_statistics.csv | results_by_count/n1000/snapshot/results\tables\feature_descriptive_statistics.csv | 4.0 | 9.0 | variable; count; mean; std; min; 25%; 50%; 75%; max | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| file_inventory_all.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\file_inventory_all.csv | 2427.0 | 11.0 | side; root; relative_path; filename; stem; extension; size_bytes; modified_at; sha256; status; scan_scope | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| file_inventory_compare.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\file_inventory_compare.csv | 2421.0 | 12.0 | filename; as_is_path; extension; as_is_size; as_is_modified; as_is_sha256; to_be_path; to_be_size; to_be_modified; to_be_sha256; status; size_diff | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| final_dataset_decision_matrix.csv | results_by_count/n1000/snapshot/results\tables\scie_submission\final_dataset_decision_matrix.csv | 3.0 | 7.0 | option; role; dataset_policy; strength; risk; recommended_use; decision | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| final_paper_quality_check.csv | results_by_count/n1000/snapshot/results\tables\final_paper_quality_check.csv | 53.0 | 6.0 | section_id; section_title; issue_type; matched_text; severity; suggested_fix | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| final_submission_board.csv | results_by_count/n1000/snapshot/results\tables\repro\final_submission_board.csv | 6.0 | 4.0 | category; status; item; detail | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| folder_extension_summary.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\folder_extension_summary.csv | 20.0 | 5.0 | side; top_folder; extension; file_count; total_size_bytes | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| future_research_action_plan.csv | results_by_count/n1000/snapshot/results\tables\future_research_action_plan.csv | 4.0 | 7.0 | future_task; related_limitation; priority; recommended_sample; method; statistics; paper_usage | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| future_research_limitations.csv | results_by_count/n1000/snapshot/results\tables\future_research_limitations.csv | 4.0 | 8.0 | id; title_ko; current_limitation; future_analysis; future_outputs; paper_position; current_data_status; future_data_needed | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| journal_target_strategy.csv | results_by_count/n1000/snapshot/results\tables\scie_submission\journal_target_strategy.csv | 3.0 | 5.0 | tier; journal_candidate; positioning; needed_before_submit; risk | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| key_metric_compare.csv | results_by_count/n1000/snapshot/results\tables\key_metric_compare.csv | 6.0 | 8.0 | metric; asis_value; tobe_value; delta; asis_source; tobe_source; status; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| key_metric_compare.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\key_metric_compare.csv | 313.0 | 10.0 | metric_key; status; as_is_value; to_be_value; diff_to_be_minus_as_is; pct_diff; as_is_source; to_be_source; as_is_note; to_be_note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| key_metrics_all.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\key_metrics_all.csv | 370.0 | 5.0 | side; metric_key; metric_value; source_file; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| kspon_manifest.csv | results_by_count/n1000/snapshot/data\kspon\manifest\kspon_manifest.csv | 100.0 | 12.0 | file_id; pcm_source_path; txt_source_path; pcm_target_path; txt_target_path; has_audio; has_transcript; file_size_kb; source_folder; dataset; audio_copied; txt_copied | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| kspon_validation_plan.csv | results_by_count/n1000/snapshot/results\tables\scie_submission\kspon_validation_plan.csv | 5.0 | 5.0 | step; minimum; recommended; output; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| kspon_wer_cer_by_file.csv | results_by_count/n1000/snapshot/results\stt_validation\kspon_wer_cer_by_file.csv | 100.0 | 8.0 | file_id; reference_path; has_reference; reference_text; hypothesis_text; wer; cer; model | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| kspon_wer_cer_summary.csv | results_by_count/n1000/snapshot/results\stt_validation\kspon_wer_cer_summary.csv | 1.0 | 9.0 | model; dataset; n; mean_wer; median_wer; mean_cer; median_cer; status; message | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| kspon_whisper_medium_results.csv | results_by_count/n1000/snapshot/data\kspon\stt\kspon_whisper_medium_results.csv | 100.0 | 14.0 | file_id; audio_path; transcript_path; transcript; model; language; device; compute_type; beam_size; vad_filter; audio_duration_sec; ok; error; elapsed_sec | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| learning_dashboard.csv | results_by_count/n1000/snapshot/results\tables\learning\learning_dashboard.csv | 4.0 | 3.0 | item; value; interpretation | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| learning_progress_score.csv | results_by_count/n1000/snapshot/results\tables\learning\learning_progress_score.csv | 1.0 | 20.0 | generated_at; profile; learning_score; status; reasons; stress_score_n; stress_score_mean; voc_stt_n; voc_stt_nonempty; wpm_nonzero_rows; feature_rows; reference_count; has_final_board; has_three_way_compare; has_claim_check; has_submission_gate; has_security_check; source_stress; source_stt; source_features | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| loo_stability.csv | results_by_count/n1000/snapshot/results\tables\loo_stability.csv | 4.0 | 7.0 | removed_variable; remaining_variables; r; r_squared; p_value; decision; p_value_display | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| loo_stability.csv | results_by_count/n1000/snapshot/results\validity\loo_stability.csv | 4.0 | 7.0 | removed_variable; remaining_variables; r; r_squared; p_value; decision; p_value_display | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| meeting_0627_current_status.csv | results_by_count/n1000/snapshot/results\tables\meeting_0627\meeting_0627_current_status.csv | 1.0 | 17.0 | profile; snapshot_found; voc_stt_n; features_n; stress_n; kspon_stt_n; wpm_nonzero; wpm_mean; quality_fail; quality_warn; corr_energy; corr_pitch; corr_wpm; corr_duration; wer; cer; generated_at | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| monitoring_summary_n1000.csv | results_by_count/n1000/snapshot/results\tables\monitoring\monitoring_summary_n1000.csv | 13.0 | 3.0 | category; metric; value | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| multicenter_generalization_status.csv | results_by_count/n1000/snapshot/results\tables\future_analysis\multicenter_generalization_status.csv | 1.0 | 4.0 | task; status; message; required_file | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| next_action_plan.csv | results_by_count/n1000/snapshot/results\tables\repro\next_action_plan.csv | 2.0 | 10.0 | run_order; issue; status; severity; evidence; likely_cause; recommended_action; menu_no; command_hint; priority | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| outlier_robustness.csv | results_by_count/n1000/snapshot/results\robustness\outlier_robustness.csv | 4.0 | 8.0 | scenario; n; energy_mean_r; pitch_mean_r; wpm_r; duration_sec_r; min_weight_sensitivity_r; decision | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| outlier_robustness.csv | results_by_count/n1000/snapshot/results\tables\outlier_robustness.csv | 4.0 | 8.0 | scenario; n; energy_mean_r; pitch_mean_r; wpm_r; duration_sec_r; min_weight_sensitivity_r; decision | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| professor_questions_0627.csv | results_by_count/n1000/snapshot/results\tables\meeting_0627\professor_questions_0627.csv | 5.0 | 2.0 | 교수님 확인 질문; 왜 필요한가 | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| reference_papers_manifest.csv | results_by_count/n1000/snapshot/results\tables\reference_papers_manifest.csv | 0.0 | 1.0 |  | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| references.csv | results_by_count/n1000/snapshot/results\tables\references.csv | 30.0 | 10.0 | no; authors; year; title; source; volume; pages; doi; type; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| research_improvement_cases_dataset_manifest.csv | results_by_count/n1000/snapshot/results\tables\learning\research_improvement_cases_dataset_manifest.csv | 2.0 | 3.0 | task; source; quality | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| result_similarity_matrix.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\result_similarity_matrix.csv | 313.0 | 9.0 | metric_key; category; as_is_value; to_be_value; abs_diff; pct_diff; similarity_status; judgement_reason; source_note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| resume_steps_n1000.csv | results_by_count/n1000/snapshot/results\tables\resume\resume_steps_n1000.csv | 6.0 | 6.0 | step_no; step; count; target; status; command | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| reviewer_qa_dataset_manifest.csv | results_by_count/n1000/snapshot/results\tables\learning\reviewer_qa_dataset_manifest.csv | 0.0 | 1.0 |  | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| sample_match_details.csv | results_by_count/n1000/snapshot/results\tables\repro\sample_match_details.csv | 3000.0 | 2.0 | sample_id; status | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| sample_match_summary.csv | results_by_count/n1000/snapshot/results\tables\repro\sample_match_summary.csv | 5.0 | 3.0 | set_name; csv_file; id_count | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| scie_current_metric_snapshot.csv | results_by_count/n1000/snapshot/results\tables\scie_submission\scie_current_metric_snapshot.csv | 1.0 | 16.0 | profile; snapshot_path; snapshot_exists; stt_path; stt_rows; stt_success_unique; stt_failed_or_empty; stt_model_detected; feature_path; feature_n; stress_path; stress_n; wpm_mean; stress_mean; generated_at; analysis_policy | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| scie_readiness_gates.csv | results_by_count/n1000/snapshot/results\tables\scie_submission\scie_readiness_gates.csv | 7.0 | 5.0 | gate; requirement; current_status; decision; action | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| scie_reproducibility_checklist.csv | results_by_count/n1000/snapshot/results\tables\scie_submission\scie_reproducibility_checklist.csv | 6.0 | 5.0 | category; item; status; evidence; owner_note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| speaker_normalization_status.csv | results_by_count/n1000/snapshot/results\tables\future_analysis\speaker_normalization_status.csv | 1.0 | 4.0 | task; status; message; required_file | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| statistical_validation_checklist.csv | results_by_count/n1000/snapshot/results\tables\scie_submission\statistical_validation_checklist.csv | 7.0 | 4.0 | analysis; needed_for_scie; status; where_to_place | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| step_status_n1000.csv | results_by_count/n1000/snapshot/results\tables\history\step_status_n1000.csv | 113.0 | 15.0 | profile; section; step_no; step_label; status; status_rank; run_count; success_count; fail_count; last_event_time; last_status; last_return_code; last_elapsed_sec; last_command; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| stress_index_feature_quality.csv | results_by_count/n1000/snapshot/results\tables\stress_index_feature_quality.csv | 4.0 | 6.0 | variable; valid_n; unique_n; mean; std_ddof0; status | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| stt_error_sensitivity.csv | results_by_count/n1000/snapshot/results\robustness\stt_error_sensitivity.csv | 6.0 | 8.0 | scenario; wpm_multiplier; correlation_with_baseline; p_value; p_value_display; mad; decision; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| stt_error_sensitivity.csv | results_by_count/n1000/snapshot/results\tables\stt_error_sensitivity.csv | 6.0 | 8.0 | scenario; wpm_multiplier; correlation_with_baseline; p_value; p_value_display; mad; decision; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| stt_reliability.csv | results_by_count/n1000/snapshot/results\tables\stt_reliability.csv | 1.0 | 9.0 | model; dataset; n; mean_wer; median_wer; mean_cer; median_cer; status; message | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| stt_wpm_reliability_sources.csv | results_by_count/n1000/snapshot/results\tables\future_analysis\stt_wpm_reliability_sources.csv | 3.0 | 3.0 | source; path; status | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| stt_wpm_reliability_status.csv | results_by_count/n1000/snapshot/results\tables\future_analysis\stt_wpm_reliability_status.csv | 1.0 | 4.0 | task; status; message; required_file | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| stt_wpm_reliability_summary.csv | results_by_count/n1000/snapshot/results\tables\future_analysis\stt_wpm_reliability_summary.csv | 1.0 | 7.0 | model; dataset; n; mean_wer; median_wer; mean_cer; median_cer | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| submission_readiness_check.csv | results_by_count/n1000/snapshot/results\tables\submission_readiness_check.csv | 19.0 | 4.0 | check; value; status; required_fix | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| submission_readiness_checklist.csv | results_by_count/n1000/snapshot/results\tables\submission_readiness_checklist.csv | 15.0 | 5.0 | item; status; severity; message; recommended_action | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| submitted_doc_numeric_contexts.csv | results_by_count/n1000/snapshot/results\tables\repro\submitted_doc_numeric_contexts.csv | 29.0 | 5.0 | docx_file; context_id; tag; value; context | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| three_way_compare.csv | results_by_count/n1000/snapshot/results\tables\repro\three_way_compare.csv | 19.0 | 15.0 | metric_key; status; document_value_candidate; as_is_value; to_be_value; to_be_minus_as_is; document_minus_tobe; docx_file; tobe_root; snapshot_found; root_fallback; stale_snapshot_detected; snapshot_stt_count; root_stt_count; interpretation | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| tobe_metric_source_trace.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\tobe_metric_source_trace.csv | 283.0 | 5.0 | side; metric_key; metric_value; source_file; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| training_examples_by_task.csv | results_by_count/n1000/snapshot/results\tables\learning\training_examples_by_task.csv | 0.0 | 2.0 | task; example_count | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| training_examples_manifest.csv | results_by_count/n1000/snapshot/results\tables\learning\training_examples_manifest.csv | 0.0 | 1.0 |  | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| tukey_hsd.csv | results_by_count/n1000/snapshot/results\tables\tukey_hsd.csv | 12.0 | 9.0 | variable; group1; group2; meandiff; p_adj; lower; upper; reject; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| tukey_hsd.csv | results_by_count/n1000/snapshot/results\validity\tukey_hsd.csv | 12.0 | 9.0 | variable; group1; group2; meandiff; p_adj; lower; upper; reject; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| validity_structure_summary.csv | results_by_count/n1000/snapshot/results\tables\validity_structure_summary.csv | 1.0 | 3.0 | convergent_mean_abs_r; auxiliary_mean_abs_r; delta | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_features.csv | results_by_count/n1000/snapshot/data\voc\features\voc_features.csv | 1000.0 | 12.0 | wav_id; audio_path; energy_mean; pitch_mean; duration_sec; transcript; word_cnt; text_len; wpm; stt_status; ok; error | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_manifest.csv | results_by_count/n1000/snapshot/data\voc\manifest\voc_manifest.csv | 1000.0 | 8.0 | file_id; source_path; target_path; file_name; extension; file_size_kb; dataset; copied | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_stress_score_rebuilt.csv | results_by_count/n1000/snapshot/results\stress_index\voc_stress_score_rebuilt.csv | 1000.0 | 18.0 | wav_id; audio_path; energy_mean; pitch_mean; duration_sec; transcript; word_cnt; text_len; wpm; stt_status; ok; error; z_energy_mean; z_pitch_mean; z_wpm; z_duration_sec; stress_raw; stress_score | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_stt_completeness.csv | results_by_count/n1000/snapshot/results\tables\voc_stt_completeness.csv | 1.0 | 6.0 | n; transcript_nonempty; word_cnt_nonzero; wpm_nonzero; stt_missing_rows; status | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_whisper_medium_results.csv | results_by_count/n1000/snapshot/data\voc\stt\voc_whisper_medium_results.csv | 8.0 | 14.0 | file_id; audio_path; transcript_path; transcript; model; language; device; compute_type; beam_size; vad_filter; audio_duration_sec; ok; error; elapsed_sec | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_whisper_small_results.csv | results_by_count/n1000/snapshot/data\voc\stt\voc_whisper_small_results.csv | 1000.0 | 14.0 | file_id; audio_path; transcript_path; transcript; model; language; device; compute_type; beam_size; vad_filter; audio_duration_sec; ok; error; elapsed_sec | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_whisper_small_results_failed_rows.csv | results_by_count/n1000/snapshot/data\voc\stt\small_failed_backup_20260702_171422\voc_whisper_small_results_failed_rows.csv | 0.0 | 1.0 |  | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_whisper_small_results_failed_rows.csv | results_by_count/n1000/snapshot/data\voc\stt\small_failed_backup_20260702_171446\voc_whisper_small_results_failed_rows.csv | 0.0 | 1.0 |  | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_whisper_small_results_failed_rows.csv | results_by_count/n1000/snapshot/data\voc\stt\small_failed_backup_20260702_173438\voc_whisper_small_results_failed_rows.csv | 1.0 | 14.0 | file_id; audio_path; transcript_path; transcript; model; language; device; compute_type; beam_size; vad_filter; audio_duration_sec; ok; error; elapsed_sec | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_whisper_small_results_original_backup.csv | results_by_count/n1000/snapshot/data\voc\stt\small_failed_backup_20260702_171422\voc_whisper_small_results_original_backup.csv | 12.0 | 14.0 | file_id; audio_path; transcript_path; transcript; model; language; device; compute_type; beam_size; vad_filter; audio_duration_sec; ok; error; elapsed_sec | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_whisper_small_results_original_backup.csv | results_by_count/n1000/snapshot/data\voc\stt\small_failed_backup_20260702_171446\voc_whisper_small_results_original_backup.csv | 12.0 | 14.0 | file_id; audio_path; transcript_path; transcript; model; language; device; compute_type; beam_size; vad_filter; audio_duration_sec; ok; error; elapsed_sec | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_whisper_small_results_original_backup.csv | results_by_count/n1000/snapshot/data\voc\stt\small_failed_backup_20260702_173438\voc_whisper_small_results_original_backup.csv | 100.0 | 14.0 | file_id; audio_path; transcript_path; transcript; model; language; device; compute_type; beam_size; vad_filter; audio_duration_sec; ok; error; elapsed_sec | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| weight_sensitivity.csv | results_by_count/n1000/snapshot/results\tables\weight_sensitivity.csv | 6.0 | 7.0 | scenario; weights_e_p_w_d; correlation_with_baseline; p_value; mad; decision; p_value_display | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| weight_sensitivity.csv | results_by_count/n1000/snapshot/results\validity\weight_sensitivity.csv | 6.0 | 7.0 | scenario; weights_e_p_w_d; correlation_with_baseline; p_value; mad; decision; p_value_display | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| why_why_why_structure.csv | results_by_count/n1000/snapshot/results\tables\meeting_0627\why_why_why_structure.csv | 6.0 | 2.0 | 질문; 답변 | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE |
산출물 CSV: research_continuity/15_asis_tobe_warn_source_fix_v169/key_metric_compare_resolved_v169.csv (행 313, 열 17)
대용량 표 요약·앞/뒤 표본 포함, 원본은 데이터 ZIP 수록
| metric_key | status | as_is_value | to_be_value | diff_to_be_minus_as_is | pct_diff | as_is_source | to_be_source | as_is_note | to_be_note | original_status | resolution_status | professor_ready_status | resolution_note | as_is_final_locked_value | legacy_gap | final_locked_gap |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| anova_F_wpm | ASIS_ONLY | 75.26227934811351 | 05_results\anova.csv | anova_profile | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| anova_eta2_wpm | ASIS_ONLY | 0.131402297361085 | 05_results\anova.csv | anova_profile | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_cols::anova.csv | ASIS_ONLY | 4.0 | 05_results\anova.csv | CSV column count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_cols::correlation_4vars.csv | ASIS_ONLY | 5.0 | 05_results\correlation_4vars.csv | CSV column count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_cols::datasets0413_clean.csv | ASIS_ONLY | 13.0 | 04_final\datasets0413_clean.csv | CSV column count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_cols::datasets0413_clean_rms.csv | ASIS_ONLY | 13.0 | 04_final\datasets0413_clean_rms.csv | CSV column count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_cols::descriptive_stats.csv | ASIS_ONLY | 9.0 | 05_results\descriptive_stats.csv | CSV column count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_cols::features_voc_0413.csv | ASIS_ONLY | 4.0 | 03_features\features_voc_0413.csv | CSV column count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_cols::features_voc_0413_rms.csv | ASIS_ONLY | 4.0 | 03_features\features_voc_0413_rms.csv | CSV column count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_cols::global_ref_rms.csv | ASIS_ONLY | 7.0 | 05_results\global_ref_rms.csv | CSV column count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_cols::leave_one_out.csv | ASIS_ONLY | 4.0 | 05_results\leave_one_out.csv | CSV column count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_cols::sensitivity.csv | ASIS_ONLY | 4.0 | 05_results\sensitivity.csv | CSV column count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_cols::stt_1000.csv | ASIS_ONLY | 7.0 | stt_1000.csv | CSV column count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_cols::tukey.csv | ASIS_ONLY | 8.0 | 05_results\tukey.csv | CSV column count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_cols::validity_summary.csv | ASIS_ONLY | 2.0 | 05_results\validity_summary.csv | CSV column count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_rows::anova.csv | ASIS_ONLY | 4.0 | 05_results\anova.csv | CSV row count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_rows::correlation_4vars.csv | ASIS_ONLY | 4.0 | 05_results\correlation_4vars.csv | CSV row count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_rows::datasets0413_clean.csv | ASIS_ONLY | 998.0 | 04_final\datasets0413_clean.csv | CSV row count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_rows::datasets0413_clean_rms.csv | ASIS_ONLY | 998.0 | 04_final\datasets0413_clean_rms.csv | CSV row count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_rows::descriptive_stats.csv | ASIS_ONLY | 7.0 | 05_results\descriptive_stats.csv | CSV row count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_rows::features_voc_0413.csv | ASIS_ONLY | 1000.0 | 03_features\features_voc_0413.csv | CSV row count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_rows::features_voc_0413_rms.csv | ASIS_ONLY | 1000.0 | 03_features\features_voc_0413_rms.csv | CSV row count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_rows::global_ref_rms.csv | ASIS_ONLY | 4.0 | 05_results\global_ref_rms.csv | CSV row count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_rows::leave_one_out.csv | ASIS_ONLY | 4.0 | 05_results\leave_one_out.csv | CSV row count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_rows::sensitivity.csv | ASIS_ONLY | 7.0 | 05_results\sensitivity.csv | CSV row count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| ... 중간 263행은 전체 데이터 부록 ZIP에 원본으로 포함 ... | ||||||||||||||||
| csv_rows::voc_whisper_small_results_failed_rows.csv | TOBE_ONLY | 0.0 | results_by_count/n1000/snapshot/data\voc\stt\small_failed_backup_20260702_171422\voc_whisper_small_results_failed_rows.csv | CSV row count | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| csv_rows::voc_whisper_small_results_original_backup.csv | TOBE_ONLY | 12.0 | results_by_count/n1000/snapshot/data\voc\stt\small_failed_backup_20260702_171422\voc_whisper_small_results_original_backup.csv | CSV row count | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| csv_rows::weight_sensitivity.csv | TOBE_ONLY | 6.0 | results_by_count/n1000/snapshot/results\tables\weight_sensitivity.csv | CSV row count | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| csv_rows::why_why_why_structure.csv | TOBE_ONLY | 6.0 | results_by_count/n1000/snapshot/results\tables\meeting_0627\why_why_why_structure.csv | CSV row count | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| kspon_mean_cer | TOBE_ONLY | 1.0 | results_by_count/n1000/snapshot/results\tables\stt_reliability.csv | stt_reliability | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| kspon_mean_wer | TOBE_ONLY | 1.0 | results_by_count/n1000/snapshot/results\tables\stt_reliability.csv | stt_reliability | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| kspon_median_cer | TOBE_ONLY | 1.0 | results_by_count/n1000/snapshot/results\tables\stt_reliability.csv | stt_reliability | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| kspon_median_wer | TOBE_ONLY | 1.0 | results_by_count/n1000/snapshot/results\tables\stt_reliability.csv | stt_reliability | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| kspon_stt_n | TOBE_ONLY | 100.0 | results_by_count/n1000/snapshot/results\tables\stt_reliability.csv | stt_reliability | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| mean_cer_count | TOBE_ONLY | 1.0 | results_by_count/n1000/snapshot/results\tables\future_analysis\stt_wpm_reliability_summary.csv | raw numeric column | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| mean_cer_max | TOBE_ONLY | 0.2050518579102737 | results_by_count/n1000/snapshot/results\tables\future_analysis\stt_wpm_reliability_summary.csv | raw numeric column | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| mean_cer_mean | TOBE_ONLY | 0.2050518579102737 | results_by_count/n1000/snapshot/results\tables\future_analysis\stt_wpm_reliability_summary.csv | raw numeric column | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| mean_cer_min | TOBE_ONLY | 0.2050518579102737 | results_by_count/n1000/snapshot/results\tables\future_analysis\stt_wpm_reliability_summary.csv | raw numeric column | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| mean_cer_std | TOBE_ONLY | 0.0 | results_by_count/n1000/snapshot/results\tables\future_analysis\stt_wpm_reliability_summary.csv | raw numeric column | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| mean_wer_count | TOBE_ONLY | 1.0 | results_by_count/n1000/snapshot/results\tables\future_analysis\stt_wpm_reliability_summary.csv | raw numeric column | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| mean_wer_max | TOBE_ONLY | 0.3878978364810636 | results_by_count/n1000/snapshot/results\tables\future_analysis\stt_wpm_reliability_summary.csv | raw numeric column | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| mean_wer_mean | TOBE_ONLY | 0.3878978364810636 | results_by_count/n1000/snapshot/results\tables\future_analysis\stt_wpm_reliability_summary.csv | raw numeric column | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| mean_wer_min | TOBE_ONLY | 0.3878978364810636 | results_by_count/n1000/snapshot/results\tables\future_analysis\stt_wpm_reliability_summary.csv | raw numeric column | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| mean_wer_std | TOBE_ONLY | 0.0 | results_by_count/n1000/snapshot/results\tables\future_analysis\stt_wpm_reliability_summary.csv | raw numeric column | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| wer_mean | TOBE_ONLY | 1.0 | results_by_count/n1000/snapshot/results\stt_validation\kspon_wer_cer_by_file.csv | wer/cer file | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| wer_median | TOBE_ONLY | 1.0 | results_by_count/n1000/snapshot/results\stt_validation\kspon_wer_cer_by_file.csv | wer/cer file | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| energy_mean_mean | RESOLVED_REVIEWED | 0.0679645110266973 | 0.0488152021351269 | -0.0191493088915703 | -0.2817545304496968 | 04_final\datasets0413_clean.csv | results_by_count/n1000/snapshot/results\stress_index\voc_stress_score_rebuilt.csv | raw numeric column | raw numeric column | selected_slot_snapshot:n1000 | RESOLVED_REVIEWED | NOT_APPLICABLE | RESOLVED_REVIEWED | ||||
| energy_mean_min | RESOLVED_REVIEWED | 0.0127306031063199 | 0.0072453501634299 | -0.00548525294289 | -0.43087141253873 | 04_final\datasets0413_clean.csv | results_by_count/n1000/snapshot/results\stress_index\voc_stress_score_rebuilt.csv | raw numeric column | raw numeric column | selected_slot_snapshot:n1000 | RESOLVED_REVIEWED | NOT_APPLICABLE | RESOLVED_REVIEWED | ||||
| energy_mean_std | RESOLVED_REVIEWED | 0.0292308259993892 | 0.0229951877097163 | -0.0062356382896729 | -0.2133240535112911 | 04_final\datasets0413_clean.csv | results_by_count/n1000/snapshot/results\stress_index\voc_stress_score_rebuilt.csv | raw numeric column | raw numeric column | selected_slot_snapshot:n1000 | RESOLVED_REVIEWED | NOT_APPLICABLE | RESOLVED_REVIEWED | ||||
| stt_transcript_empty_rate | RESOLVED_REVIEWED | 0.003 | 0.0 | -0.003 | -1.0 | stt_1000.csv | results_by_count/n1000/snapshot/data\voc\stt\voc_whisper_small_results.csv | column=text | column=transcript_path | selected_slot_snapshot:n1000 | RESOLVED_REVIEWED | NOT_APPLICABLE | RESOLVED_REVIEWED |
산출물 CSV: research_continuity/15_asis_tobe_warn_source_fix_v169/strict_original_csv_schema_compare_v169.csv (행 138, 열 17)
전체 행 포함
| pair_key | filename | as_is_path | as_is_rows | as_is_cols | as_is_columns | as_is_status | to_be_path | to_be_rows | to_be_cols | to_be_columns | to_be_status | status | original_status | professor_ready_status | resolution_status | resolution_note |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| anova.csv | anova.csv | 05_results\anova.csv | 4.0 | 4.0 | variable; F; p_value; eta_squared | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| correlation_4vars.csv | correlation_4vars.csv | 05_results\correlation_4vars.csv | 4.0 | 5.0 | metric; x; value; p_value; n | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| datasets0413_clean.csv | datasets0413_clean.csv | 04_final\datasets0413_clean.csv | 998.0 | 13.0 | wav_id; energy_mean; pitch_mean; duration_sec; word_cnt; wpm; text_len; z_energy_mean; z_pitch_mean; z_wpm; z_duration_sec; raw_stress; stress_score | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| datasets0413_clean_rms.csv | datasets0413_clean_rms.csv | 04_final\datasets0413_clean_rms.csv | 998.0 | 13.0 | wav_id; energy_mean; pitch_mean; duration_sec; word_cnt; text_len; wpm; z_energy_mean; z_pitch_mean; z_wpm; z_duration_sec; raw_stress; stress_score | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| descriptive_stats.csv | descriptive_stats.csv | 05_results\descriptive_stats.csv | 7.0 | 9.0 | Unnamed: 0; count; mean; std; min; 25%; 50%; 75%; max | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| features_voc_0413.csv | features_voc_0413.csv | 03_features\features_voc_0413.csv | 1000.0 | 4.0 | wav_id; energy_mean; pitch_mean; duration_sec | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| features_voc_0413_rms.csv | features_voc_0413_rms.csv | 03_features\features_voc_0413_rms.csv | 1000.0 | 4.0 | wav_id; energy_mean; pitch_mean; duration_sec | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| global_ref_rms.csv | global_ref_rms.csv | 05_results\global_ref_rms.csv | 4.0 | 7.0 | variable; mu; sigma; min; max; method; source | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| leave_one_out.csv | leave_one_out.csv | 05_results\leave_one_out.csv | 4.0 | 4.0 | dropped_variable; corr_with_full; p_value; mean_abs_diff | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| sensitivity.csv | sensitivity.csv | 05_results\sensitivity.csv | 7.0 | 4.0 | scenario; corr_with_base; p_value; mean_abs_diff | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| stt_1000.csv | stt_1000.csv | stt_1000.csv | 1000.0 | 7.0 | wav_id; text; word_cnt; text_len; source; stt_ok; sample_order | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| tukey.csv | tukey.csv | 05_results\tukey.csv | 12.0 | 8.0 | variable; group1; group2; meandiff; p_adj; lower; upper; reject | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| validity_summary.csv | validity_summary.csv | 05_results\validity_summary.csv | 3.0 | 2.0 | item; value | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| discriminant_details.csv | discriminant_details.csv | 05_results\discriminant_details.csv | 4.0 | 4.0 | variable; r; p_value; abs_r | ok | results_by_count/n1000/snapshot/results\tables\discriminant_details.csv | 4.0 | 4.0 | variable; r; p_value; abs_r | ok | MATCH | MATCH | MATCH | NOT_APPLICABLE | |
| discriminant_details.csv | discriminant_details.csv | 05_results\discriminant_details.csv | 4.0 | 4.0 | variable; r; p_value; abs_r | ok | results_by_count/n1000/snapshot/results\validity\discriminant_details.csv | 4.0 | 4.0 | variable; r; p_value; abs_r | ok | MATCH | MATCH | MATCH | NOT_APPLICABLE | |
| bootstrap_ci.csv | bootstrap_ci.csv | 05_results\bootstrap_ci.csv | 4.0 | 5.0 | variable; boot_mean_r; ci_low_2_5; ci_high_97_5; n_boot | ok | results_by_count/n1000/snapshot/results\tables\bootstrap_ci.csv | 4.0 | 5.0 | variable; r; ci_lower; ci_upper; n_boot | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |
| bootstrap_ci.csv | bootstrap_ci.csv | 05_results\bootstrap_ci.csv | 4.0 | 5.0 | variable; boot_mean_r; ci_low_2_5; ci_high_97_5; n_boot | ok | results_by_count/n1000/snapshot/results\validity\bootstrap_ci.csv | 4.0 | 5.0 | variable; r; ci_lower; ci_upper; n_boot | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |
| correlation_matrix.csv | correlation_matrix.csv | 05_results\correlation_matrix.csv | 7.0 | 8.0 | Unnamed: 0; energy_mean; pitch_mean; wpm; duration_sec; stress_score; word_cnt; text_len | ok | results_by_count/n1000/snapshot/results\tables\correlation_matrix.csv | 7.0 | 8.0 | variable; stress_score; energy_mean; pitch_mean; wpm; duration_sec; word_cnt; text_len | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |
| correlation_matrix.csv | correlation_matrix.csv | 05_results\correlation_matrix.csv | 7.0 | 8.0 | Unnamed: 0; energy_mean; pitch_mean; wpm; duration_sec; stress_score; word_cnt; text_len | ok | results_by_count/n1000/snapshot/results\validity\correlation_matrix.csv | 7.0 | 8.0 | variable; stress_score; energy_mean; pitch_mean; wpm; duration_sec; word_cnt; text_len | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |
| multivariate_ols.csv | multivariate_ols.csv | 05_results\multivariate_ols.csv | 5.0 | 8.0 | term; coef; std_err; t; p_value; r2; adj_r2; n | ok | results_by_count/n1000/snapshot/results\tables\multivariate_ols.csv | 4.0 | 9.0 | term; coef; std_err; t; p_value; r2; adj_r2; n; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |
| multivariate_ols.csv | multivariate_ols.csv | 05_results\multivariate_ols.csv | 5.0 | 8.0 | term; coef; std_err; t; p_value; r2; adj_r2; n | ok | results_by_count/n1000/snapshot/results\validity\multivariate_ols.csv | 4.0 | 9.0 | term; coef; std_err; t; p_value; r2; adj_r2; n; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |
| univariate_ols.csv | univariate_ols.csv | 05_results\univariate_ols.csv | 4.0 | 7.0 | model; intercept; slope; r2; t_slope; p_slope; n | ok | results_by_count/n1000/snapshot/results\tables\univariate_ols.csv | 4.0 | 8.0 | model; intercept; slope; r2; t_slope; p_slope; n; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |
| univariate_ols.csv | univariate_ols.csv | 05_results\univariate_ols.csv | 4.0 | 7.0 | model; intercept; slope; r2; t_slope; p_slope; n | ok | results_by_count/n1000/snapshot/results\validity\univariate_ols.csv | 4.0 | 8.0 | model; intercept; slope; r2; t_slope; p_slope; n; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |
| vif.csv | vif.csv | 05_results\vif.csv | 4.0 | 2.0 | variable; VIF | ok | results_by_count/n1000/snapshot/results\tables\vif.csv | 4.0 | 3.0 | variable; VIF; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |
| vif.csv | vif.csv | 05_results\vif.csv | 4.0 | 2.0 | variable; VIF | ok | results_by_count/n1000/snapshot/results\validity\vif.csv | 4.0 | 3.0 | variable; VIF; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |
| additional_research_execution_order.csv | results_by_count/n1000/snapshot/results\tables\future_analysis\additional_research_execution_order.csv | 6.0 | 5.0 | order; step; menu; action; required_now | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| additional_research_plan.csv | results_by_count/n1000/snapshot/results\tables\future_analysis\additional_research_plan.csv | 5.0 | 9.0 | stage; topic; current_limitation; required_data; recommended_sample; method; expected_output; paper_usage; priority | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| additional_research_required_data.csv | results_by_count/n1000/snapshot/results\tables\future_analysis\additional_research_required_data.csv | 5.0 | 4.0 | data_item; columns; needed_for; current_template | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| additional_research_status.csv | results_by_count/n1000/snapshot/results\tables\future_analysis\additional_research_status.csv | 1.0 | 5.0 | task; status; stress_score_n; stt_reliability_available; message | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| additional_research_why_method_direction.csv | results_by_count/n1000/snapshot/results\tables\meeting_0627\additional_research_why_method_direction.csv | 5.0 | 5.0 | 주제; 왜 필요한가; 왜 이 기법인가; 방향성; 이번 보고에서의 표현 | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| aiis_branch_overview.csv | results_by_count/n1000/snapshot/results\tables\aiis_branch_overview.csv | 2.0 | 5.0 | branch_name; branch_dir; manifest_exists; status_exists; updated_at | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| aiis_final_diff_actions.csv | results_by_count/n1000/snapshot/results\tables\aiis_final_diff_actions.csv | 2.0 | 4.0 | priority; action; command_or_screen; done_when | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| aiis_final_diff_metrics.csv | results_by_count/n1000/snapshot/results\tables\aiis_final_diff_metrics.csv | 10.0 | 8.0 | metric; asis_value; tobe_value; delta; unit; status; reason; source | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| aiis_final_diff_overview.csv | results_by_count/n1000/snapshot/results\tables\aiis_final_diff_overview.csv | 9.0 | 3.0 | item; value; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| aiis_final_diff_root_causes.csv | results_by_count/n1000/snapshot/results\tables\aiis_final_diff_root_causes.csv | 5.0 | 7.0 | priority; level; title; evidence; why_it_happened; paper_impact; next_action | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| aiis_key_metric_compare.csv | results_by_count/n1000/snapshot/results\tables\aiis_key_metric_compare.csv | 6.0 | 8.0 | metric; asis_value; tobe_value; delta; asis_source; tobe_source; status; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| aiis_recommended_actions.csv | results_by_count/n1000/snapshot/results\tables\aiis_recommended_actions.csv | 1.0 | 7.0 | order; action_code; action_name; purpose; command_hint; dashboard_path; expected_result | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| aiis_root_causes.csv | results_by_count/n1000/snapshot/results\tables\aiis_root_causes.csv | 1.0 | 10.0 | cause_code; severity; title; problem; why; evidence; paper_impact; check_next; recommended_action_ref; source | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| aiis_snapshot_status.csv | results_by_count/n1000/snapshot/results\tables\aiis_snapshot_status.csv | 1.0 | 12.0 | slot; slot_snapshot_found; tobe_scan_roots; aiis_extension_status; core_message; asis_stt_n; tobe_stt_n; asis_feature_n; tobe_feature_n; asis_stress_n; tobe_stress_n; valid_tobe_stt_source | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| aiis_tracking_metrics.csv | results_by_count/n1000/snapshot/results\tables\aiis_tracking_metrics.csv | 4.0 | 5.0 | tracking_metric; target; current_value; status; source | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| anova_group_descriptive.csv | results_by_count/n1000/snapshot/results\tables\anova_group_descriptive.csv | 3.0 | 16.0 | stress_group; stress_score_count; stress_score_mean; stress_score_std; energy_mean_count; energy_mean_mean; energy_mean_std; pitch_mean_count; pitch_mean_mean; pitch_mean_std; wpm_count; wpm_mean; wpm_std; duration_sec_count; duration_sec_mean; duration_sec_std | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| anova_profile.csv | results_by_count/n1000/snapshot/results\tables\anova_profile.csv | 4.0 | 7.0 | variable; F; p_value; eta_squared; effect_size; note; p_value_display | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| anova_profile.csv | results_by_count/n1000/snapshot/results\validity\anova_profile.csv | 4.0 | 7.0 | variable; F; p_value; eta_squared; effect_size; note; p_value_display | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| applied_sciences_required_sections.csv | results_by_count/n1000/snapshot/results\tables\applied_sciences_required_sections.csv | 20.0 | 3.0 | section_or_statement; source_or_field; status | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| artifact_lineage.csv | results_by_count/n1000/snapshot/results\tables\repro\artifact_lineage.csv | 9.0 | 8.0 | artifact; depends_on; source_stage; upstream_1; upstream_2; upstream_3; upstream_4; risk_if_changed | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| auto_execution_plan.csv | results_by_count/n1000/snapshot/results\tables\repro\auto_execution_plan.csv | 2.0 | 10.0 | run_order; issue; status; severity; evidence; likely_cause; recommended_action; menu_no; command_hint; priority | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| claim_strength_check.csv | results_by_count/n1000/snapshot/results\tables\ops\claim_strength_check.csv | 10.0 | 5.0 | risk_phrase; recommended_expression; found_count; files; decision | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| complete_report_checklist.csv | results_by_count/n1000/snapshot/results\tables\complete_report_checklist.csv | 34.0 | 10.0 | order; group; title; kind; required; exists; rows_or_note; path; status; description | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| construct_validity_summary.csv | results_by_count/n1000/snapshot/results\tables\construct_validity_summary.csv | 7.0 | 3.0 | indicator; value; decision | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| correlation_validity.csv | results_by_count/n1000/snapshot/results\tables\correlation_validity.csv | 4.0 | 6.0 | variable; r; p_value; validity_type; decision; p_value_display | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| criterion_validity_status.csv | results_by_count/n1000/snapshot/results\tables\future_analysis\criterion_validity_status.csv | 1.0 | 4.0 | task; status; message; required_file | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| csv_profile_all.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\csv_profile_all.csv | 144.0 | 9.0 | side; relative_path; filename; stem; rows; cols; columns; status; scan_scope | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| csv_schema_compare.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\csv_schema_compare.csv | 138.0 | 13.0 | pair_key; filename; as_is_path; as_is_rows; as_is_cols; as_is_columns; as_is_status; to_be_path; to_be_rows; to_be_cols; to_be_columns; to_be_status; status | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| data_quality_action_plan.csv | results_by_count/n1000/snapshot/results\tables\data_quality_action_plan.csv | 3.0 | 7.0 | priority; action_code; action_name; trigger_items; reason; auto_executable; command_hint | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| data_quality_status.csv | results_by_count/n1000/snapshot/results\tables\data_quality_status.csv | 9.0 | 7.0 | check_item; status; value; criterion; message; recommended_action; severity | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| deepseek_learning_feedback.csv | results_by_count/n1000/snapshot/results\tables\learning\deepseek_learning_feedback.csv | 5.0 | 3.0 | area; status; recommendation | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| deploy_result_integrity.csv | results_by_count/n1000/snapshot/results\tables\deploy_result_integrity.csv | 22.0 | 7.0 | artifact; source_type; path; exists; rows_or_exists; mtime; size_kb | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| descriptive_statistics.csv | results_by_count/n1000/snapshot/results\tables\descriptive_statistics.csv | 5.0 | 9.0 | variable; count; mean; std; min; 25%; 50%; 75%; max | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| diagnosis.csv | results_by_count/n1000/snapshot/results\tables\repro\diagnosis.csv | 4.0 | 8.0 | issue; status; severity; evidence; likely_cause; recommended_action; menu_no; command_hint | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| difference_action_plan.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\difference_action_plan.csv | 2.0 | 5.0 | priority; action; why; evidence; target_menu | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| difference_problem_trace.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\difference_problem_trace.csv | 19.0 | 6.0 | metric_key; status; as_is_value; to_be_value; as_is_source; to_be_source | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| difference_root_cause_analysis.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\difference_root_cause_analysis.csv | 2.0 | 8.0 | priority; domain; severity; finding; evidence; likely_cause; next_check; recommended_action | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| failed_file_inventory.csv | results_by_count/n1000/snapshot/results\tables\ops\failed_file_inventory.csv | 600.0 | 9.0 | profile; dataset; source_csv; file_id; file; status; error; ok; transcript_len | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| feature_descriptive_statistics.csv | results_by_count/n1000/snapshot/results\tables\feature_descriptive_statistics.csv | 4.0 | 9.0 | variable; count; mean; std; min; 25%; 50%; 75%; max | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| file_inventory_all.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\file_inventory_all.csv | 2427.0 | 11.0 | side; root; relative_path; filename; stem; extension; size_bytes; modified_at; sha256; status; scan_scope | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| file_inventory_compare.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\file_inventory_compare.csv | 2421.0 | 12.0 | filename; as_is_path; extension; as_is_size; as_is_modified; as_is_sha256; to_be_path; to_be_size; to_be_modified; to_be_sha256; status; size_diff | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| final_dataset_decision_matrix.csv | results_by_count/n1000/snapshot/results\tables\scie_submission\final_dataset_decision_matrix.csv | 3.0 | 7.0 | option; role; dataset_policy; strength; risk; recommended_use; decision | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| final_paper_quality_check.csv | results_by_count/n1000/snapshot/results\tables\final_paper_quality_check.csv | 53.0 | 6.0 | section_id; section_title; issue_type; matched_text; severity; suggested_fix | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| final_submission_board.csv | results_by_count/n1000/snapshot/results\tables\repro\final_submission_board.csv | 6.0 | 4.0 | category; status; item; detail | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| folder_extension_summary.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\folder_extension_summary.csv | 20.0 | 5.0 | side; top_folder; extension; file_count; total_size_bytes | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| future_research_action_plan.csv | results_by_count/n1000/snapshot/results\tables\future_research_action_plan.csv | 4.0 | 7.0 | future_task; related_limitation; priority; recommended_sample; method; statistics; paper_usage | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| future_research_limitations.csv | results_by_count/n1000/snapshot/results\tables\future_research_limitations.csv | 4.0 | 8.0 | id; title_ko; current_limitation; future_analysis; future_outputs; paper_position; current_data_status; future_data_needed | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| journal_target_strategy.csv | results_by_count/n1000/snapshot/results\tables\scie_submission\journal_target_strategy.csv | 3.0 | 5.0 | tier; journal_candidate; positioning; needed_before_submit; risk | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| key_metric_compare.csv | results_by_count/n1000/snapshot/results\tables\key_metric_compare.csv | 6.0 | 8.0 | metric; asis_value; tobe_value; delta; asis_source; tobe_source; status; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| key_metric_compare.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\key_metric_compare.csv | 313.0 | 10.0 | metric_key; status; as_is_value; to_be_value; diff_to_be_minus_as_is; pct_diff; as_is_source; to_be_source; as_is_note; to_be_note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| key_metrics_all.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\key_metrics_all.csv | 370.0 | 5.0 | side; metric_key; metric_value; source_file; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| kspon_manifest.csv | results_by_count/n1000/snapshot/data\kspon\manifest\kspon_manifest.csv | 100.0 | 12.0 | file_id; pcm_source_path; txt_source_path; pcm_target_path; txt_target_path; has_audio; has_transcript; file_size_kb; source_folder; dataset; audio_copied; txt_copied | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| kspon_validation_plan.csv | results_by_count/n1000/snapshot/results\tables\scie_submission\kspon_validation_plan.csv | 5.0 | 5.0 | step; minimum; recommended; output; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| kspon_wer_cer_by_file.csv | results_by_count/n1000/snapshot/results\stt_validation\kspon_wer_cer_by_file.csv | 100.0 | 8.0 | file_id; reference_path; has_reference; reference_text; hypothesis_text; wer; cer; model | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| kspon_wer_cer_summary.csv | results_by_count/n1000/snapshot/results\stt_validation\kspon_wer_cer_summary.csv | 1.0 | 9.0 | model; dataset; n; mean_wer; median_wer; mean_cer; median_cer; status; message | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| kspon_whisper_medium_results.csv | results_by_count/n1000/snapshot/data\kspon\stt\kspon_whisper_medium_results.csv | 100.0 | 14.0 | file_id; audio_path; transcript_path; transcript; model; language; device; compute_type; beam_size; vad_filter; audio_duration_sec; ok; error; elapsed_sec | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| learning_dashboard.csv | results_by_count/n1000/snapshot/results\tables\learning\learning_dashboard.csv | 4.0 | 3.0 | item; value; interpretation | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| learning_progress_score.csv | results_by_count/n1000/snapshot/results\tables\learning\learning_progress_score.csv | 1.0 | 20.0 | generated_at; profile; learning_score; status; reasons; stress_score_n; stress_score_mean; voc_stt_n; voc_stt_nonempty; wpm_nonzero_rows; feature_rows; reference_count; has_final_board; has_three_way_compare; has_claim_check; has_submission_gate; has_security_check; source_stress; source_stt; source_features | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| loo_stability.csv | results_by_count/n1000/snapshot/results\tables\loo_stability.csv | 4.0 | 7.0 | removed_variable; remaining_variables; r; r_squared; p_value; decision; p_value_display | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| loo_stability.csv | results_by_count/n1000/snapshot/results\validity\loo_stability.csv | 4.0 | 7.0 | removed_variable; remaining_variables; r; r_squared; p_value; decision; p_value_display | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| meeting_0627_current_status.csv | results_by_count/n1000/snapshot/results\tables\meeting_0627\meeting_0627_current_status.csv | 1.0 | 17.0 | profile; snapshot_found; voc_stt_n; features_n; stress_n; kspon_stt_n; wpm_nonzero; wpm_mean; quality_fail; quality_warn; corr_energy; corr_pitch; corr_wpm; corr_duration; wer; cer; generated_at | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| monitoring_summary_n1000.csv | results_by_count/n1000/snapshot/results\tables\monitoring\monitoring_summary_n1000.csv | 13.0 | 3.0 | category; metric; value | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| multicenter_generalization_status.csv | results_by_count/n1000/snapshot/results\tables\future_analysis\multicenter_generalization_status.csv | 1.0 | 4.0 | task; status; message; required_file | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| next_action_plan.csv | results_by_count/n1000/snapshot/results\tables\repro\next_action_plan.csv | 2.0 | 10.0 | run_order; issue; status; severity; evidence; likely_cause; recommended_action; menu_no; command_hint; priority | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| outlier_robustness.csv | results_by_count/n1000/snapshot/results\robustness\outlier_robustness.csv | 4.0 | 8.0 | scenario; n; energy_mean_r; pitch_mean_r; wpm_r; duration_sec_r; min_weight_sensitivity_r; decision | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| outlier_robustness.csv | results_by_count/n1000/snapshot/results\tables\outlier_robustness.csv | 4.0 | 8.0 | scenario; n; energy_mean_r; pitch_mean_r; wpm_r; duration_sec_r; min_weight_sensitivity_r; decision | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| professor_questions_0627.csv | results_by_count/n1000/snapshot/results\tables\meeting_0627\professor_questions_0627.csv | 5.0 | 2.0 | 교수님 확인 질문; 왜 필요한가 | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| reference_papers_manifest.csv | results_by_count/n1000/snapshot/results\tables\reference_papers_manifest.csv | 0.0 | 1.0 |  | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| references.csv | results_by_count/n1000/snapshot/results\tables\references.csv | 30.0 | 10.0 | no; authors; year; title; source; volume; pages; doi; type; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| research_improvement_cases_dataset_manifest.csv | results_by_count/n1000/snapshot/results\tables\learning\research_improvement_cases_dataset_manifest.csv | 2.0 | 3.0 | task; source; quality | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| result_similarity_matrix.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\result_similarity_matrix.csv | 313.0 | 9.0 | metric_key; category; as_is_value; to_be_value; abs_diff; pct_diff; similarity_status; judgement_reason; source_note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| resume_steps_n1000.csv | results_by_count/n1000/snapshot/results\tables\resume\resume_steps_n1000.csv | 6.0 | 6.0 | step_no; step; count; target; status; command | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| reviewer_qa_dataset_manifest.csv | results_by_count/n1000/snapshot/results\tables\learning\reviewer_qa_dataset_manifest.csv | 0.0 | 1.0 |  | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| sample_match_details.csv | results_by_count/n1000/snapshot/results\tables\repro\sample_match_details.csv | 3000.0 | 2.0 | sample_id; status | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| sample_match_summary.csv | results_by_count/n1000/snapshot/results\tables\repro\sample_match_summary.csv | 5.0 | 3.0 | set_name; csv_file; id_count | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| scie_current_metric_snapshot.csv | results_by_count/n1000/snapshot/results\tables\scie_submission\scie_current_metric_snapshot.csv | 1.0 | 16.0 | profile; snapshot_path; snapshot_exists; stt_path; stt_rows; stt_success_unique; stt_failed_or_empty; stt_model_detected; feature_path; feature_n; stress_path; stress_n; wpm_mean; stress_mean; generated_at; analysis_policy | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| scie_readiness_gates.csv | results_by_count/n1000/snapshot/results\tables\scie_submission\scie_readiness_gates.csv | 7.0 | 5.0 | gate; requirement; current_status; decision; action | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| scie_reproducibility_checklist.csv | results_by_count/n1000/snapshot/results\tables\scie_submission\scie_reproducibility_checklist.csv | 6.0 | 5.0 | category; item; status; evidence; owner_note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| speaker_normalization_status.csv | results_by_count/n1000/snapshot/results\tables\future_analysis\speaker_normalization_status.csv | 1.0 | 4.0 | task; status; message; required_file | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| statistical_validation_checklist.csv | results_by_count/n1000/snapshot/results\tables\scie_submission\statistical_validation_checklist.csv | 7.0 | 4.0 | analysis; needed_for_scie; status; where_to_place | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| step_status_n1000.csv | results_by_count/n1000/snapshot/results\tables\history\step_status_n1000.csv | 113.0 | 15.0 | profile; section; step_no; step_label; status; status_rank; run_count; success_count; fail_count; last_event_time; last_status; last_return_code; last_elapsed_sec; last_command; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| stress_index_feature_quality.csv | results_by_count/n1000/snapshot/results\tables\stress_index_feature_quality.csv | 4.0 | 6.0 | variable; valid_n; unique_n; mean; std_ddof0; status | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| stt_error_sensitivity.csv | results_by_count/n1000/snapshot/results\robustness\stt_error_sensitivity.csv | 6.0 | 8.0 | scenario; wpm_multiplier; correlation_with_baseline; p_value; p_value_display; mad; decision; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| stt_error_sensitivity.csv | results_by_count/n1000/snapshot/results\tables\stt_error_sensitivity.csv | 6.0 | 8.0 | scenario; wpm_multiplier; correlation_with_baseline; p_value; p_value_display; mad; decision; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| stt_reliability.csv | results_by_count/n1000/snapshot/results\tables\stt_reliability.csv | 1.0 | 9.0 | model; dataset; n; mean_wer; median_wer; mean_cer; median_cer; status; message | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| stt_wpm_reliability_sources.csv | results_by_count/n1000/snapshot/results\tables\future_analysis\stt_wpm_reliability_sources.csv | 3.0 | 3.0 | source; path; status | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| stt_wpm_reliability_status.csv | results_by_count/n1000/snapshot/results\tables\future_analysis\stt_wpm_reliability_status.csv | 1.0 | 4.0 | task; status; message; required_file | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| stt_wpm_reliability_summary.csv | results_by_count/n1000/snapshot/results\tables\future_analysis\stt_wpm_reliability_summary.csv | 1.0 | 7.0 | model; dataset; n; mean_wer; median_wer; mean_cer; median_cer | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| submission_readiness_check.csv | results_by_count/n1000/snapshot/results\tables\submission_readiness_check.csv | 19.0 | 4.0 | check; value; status; required_fix | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| submission_readiness_checklist.csv | results_by_count/n1000/snapshot/results\tables\submission_readiness_checklist.csv | 15.0 | 5.0 | item; status; severity; message; recommended_action | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| submitted_doc_numeric_contexts.csv | results_by_count/n1000/snapshot/results\tables\repro\submitted_doc_numeric_contexts.csv | 29.0 | 5.0 | docx_file; context_id; tag; value; context | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| three_way_compare.csv | results_by_count/n1000/snapshot/results\tables\repro\three_way_compare.csv | 19.0 | 15.0 | metric_key; status; document_value_candidate; as_is_value; to_be_value; to_be_minus_as_is; document_minus_tobe; docx_file; tobe_root; snapshot_found; root_fallback; stale_snapshot_detected; snapshot_stt_count; root_stt_count; interpretation | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| tobe_metric_source_trace.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\tobe_metric_source_trace.csv | 283.0 | 5.0 | side; metric_key; metric_value; source_file; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| training_examples_by_task.csv | results_by_count/n1000/snapshot/results\tables\learning\training_examples_by_task.csv | 0.0 | 2.0 | task; example_count | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| training_examples_manifest.csv | results_by_count/n1000/snapshot/results\tables\learning\training_examples_manifest.csv | 0.0 | 1.0 |  | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| tukey_hsd.csv | results_by_count/n1000/snapshot/results\tables\tukey_hsd.csv | 12.0 | 9.0 | variable; group1; group2; meandiff; p_adj; lower; upper; reject; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| tukey_hsd.csv | results_by_count/n1000/snapshot/results\validity\tukey_hsd.csv | 12.0 | 9.0 | variable; group1; group2; meandiff; p_adj; lower; upper; reject; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| validity_structure_summary.csv | results_by_count/n1000/snapshot/results\tables\validity_structure_summary.csv | 1.0 | 3.0 | convergent_mean_abs_r; auxiliary_mean_abs_r; delta | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_features.csv | results_by_count/n1000/snapshot/data\voc\features\voc_features.csv | 1000.0 | 12.0 | wav_id; audio_path; energy_mean; pitch_mean; duration_sec; transcript; word_cnt; text_len; wpm; stt_status; ok; error | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_manifest.csv | results_by_count/n1000/snapshot/data\voc\manifest\voc_manifest.csv | 1000.0 | 8.0 | file_id; source_path; target_path; file_name; extension; file_size_kb; dataset; copied | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_stress_score_rebuilt.csv | results_by_count/n1000/snapshot/results\stress_index\voc_stress_score_rebuilt.csv | 1000.0 | 18.0 | wav_id; audio_path; energy_mean; pitch_mean; duration_sec; transcript; word_cnt; text_len; wpm; stt_status; ok; error; z_energy_mean; z_pitch_mean; z_wpm; z_duration_sec; stress_raw; stress_score | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_stt_completeness.csv | results_by_count/n1000/snapshot/results\tables\voc_stt_completeness.csv | 1.0 | 6.0 | n; transcript_nonempty; word_cnt_nonzero; wpm_nonzero; stt_missing_rows; status | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_whisper_medium_results.csv | results_by_count/n1000/snapshot/data\voc\stt\voc_whisper_medium_results.csv | 8.0 | 14.0 | file_id; audio_path; transcript_path; transcript; model; language; device; compute_type; beam_size; vad_filter; audio_duration_sec; ok; error; elapsed_sec | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_whisper_small_results.csv | results_by_count/n1000/snapshot/data\voc\stt\voc_whisper_small_results.csv | 1000.0 | 14.0 | file_id; audio_path; transcript_path; transcript; model; language; device; compute_type; beam_size; vad_filter; audio_duration_sec; ok; error; elapsed_sec | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_whisper_small_results_failed_rows.csv | results_by_count/n1000/snapshot/data\voc\stt\small_failed_backup_20260702_171422\voc_whisper_small_results_failed_rows.csv | 0.0 | 1.0 |  | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_whisper_small_results_failed_rows.csv | results_by_count/n1000/snapshot/data\voc\stt\small_failed_backup_20260702_171446\voc_whisper_small_results_failed_rows.csv | 0.0 | 1.0 |  | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_whisper_small_results_failed_rows.csv | results_by_count/n1000/snapshot/data\voc\stt\small_failed_backup_20260702_173438\voc_whisper_small_results_failed_rows.csv | 1.0 | 14.0 | file_id; audio_path; transcript_path; transcript; model; language; device; compute_type; beam_size; vad_filter; audio_duration_sec; ok; error; elapsed_sec | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_whisper_small_results_original_backup.csv | results_by_count/n1000/snapshot/data\voc\stt\small_failed_backup_20260702_171422\voc_whisper_small_results_original_backup.csv | 12.0 | 14.0 | file_id; audio_path; transcript_path; transcript; model; language; device; compute_type; beam_size; vad_filter; audio_duration_sec; ok; error; elapsed_sec | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_whisper_small_results_original_backup.csv | results_by_count/n1000/snapshot/data\voc\stt\small_failed_backup_20260702_171446\voc_whisper_small_results_original_backup.csv | 12.0 | 14.0 | file_id; audio_path; transcript_path; transcript; model; language; device; compute_type; beam_size; vad_filter; audio_duration_sec; ok; error; elapsed_sec | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_whisper_small_results_original_backup.csv | results_by_count/n1000/snapshot/data\voc\stt\small_failed_backup_20260702_173438\voc_whisper_small_results_original_backup.csv | 100.0 | 14.0 | file_id; audio_path; transcript_path; transcript; model; language; device; compute_type; beam_size; vad_filter; audio_duration_sec; ok; error; elapsed_sec | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| weight_sensitivity.csv | results_by_count/n1000/snapshot/results\tables\weight_sensitivity.csv | 6.0 | 7.0 | scenario; weights_e_p_w_d; correlation_with_baseline; p_value; mad; decision; p_value_display | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| weight_sensitivity.csv | results_by_count/n1000/snapshot/results\validity\weight_sensitivity.csv | 6.0 | 7.0 | scenario; weights_e_p_w_d; correlation_with_baseline; p_value; mad; decision; p_value_display | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| why_why_why_structure.csv | results_by_count/n1000/snapshot/results\tables\meeting_0627\why_why_why_structure.csv | 6.0 | 2.0 | 질문; 답변 | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE |
산출물 CSV: research_continuity/15_asis_tobe_warn_source_fix_v169/strict_original_key_metric_compare_v169.csv (행 313, 열 17)
대용량 표 요약·앞/뒤 표본 포함, 원본은 데이터 ZIP 수록
| metric_key | status | as_is_value | to_be_value | diff_to_be_minus_as_is | pct_diff | as_is_source | to_be_source | as_is_note | to_be_note | original_status | resolution_status | professor_ready_status | resolution_note | as_is_final_locked_value | legacy_gap | final_locked_gap |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| anova_F_wpm | ASIS_ONLY | 75.26227934811351 | 05_results\anova.csv | anova_profile | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| anova_eta2_wpm | ASIS_ONLY | 0.131402297361085 | 05_results\anova.csv | anova_profile | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_cols::anova.csv | ASIS_ONLY | 4.0 | 05_results\anova.csv | CSV column count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_cols::correlation_4vars.csv | ASIS_ONLY | 5.0 | 05_results\correlation_4vars.csv | CSV column count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_cols::datasets0413_clean.csv | ASIS_ONLY | 13.0 | 04_final\datasets0413_clean.csv | CSV column count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_cols::datasets0413_clean_rms.csv | ASIS_ONLY | 13.0 | 04_final\datasets0413_clean_rms.csv | CSV column count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_cols::descriptive_stats.csv | ASIS_ONLY | 9.0 | 05_results\descriptive_stats.csv | CSV column count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_cols::features_voc_0413.csv | ASIS_ONLY | 4.0 | 03_features\features_voc_0413.csv | CSV column count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_cols::features_voc_0413_rms.csv | ASIS_ONLY | 4.0 | 03_features\features_voc_0413_rms.csv | CSV column count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_cols::global_ref_rms.csv | ASIS_ONLY | 7.0 | 05_results\global_ref_rms.csv | CSV column count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_cols::leave_one_out.csv | ASIS_ONLY | 4.0 | 05_results\leave_one_out.csv | CSV column count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_cols::sensitivity.csv | ASIS_ONLY | 4.0 | 05_results\sensitivity.csv | CSV column count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_cols::stt_1000.csv | ASIS_ONLY | 7.0 | stt_1000.csv | CSV column count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_cols::tukey.csv | ASIS_ONLY | 8.0 | 05_results\tukey.csv | CSV column count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_cols::validity_summary.csv | ASIS_ONLY | 2.0 | 05_results\validity_summary.csv | CSV column count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_rows::anova.csv | ASIS_ONLY | 4.0 | 05_results\anova.csv | CSV row count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_rows::correlation_4vars.csv | ASIS_ONLY | 4.0 | 05_results\correlation_4vars.csv | CSV row count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_rows::datasets0413_clean.csv | ASIS_ONLY | 998.0 | 04_final\datasets0413_clean.csv | CSV row count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_rows::datasets0413_clean_rms.csv | ASIS_ONLY | 998.0 | 04_final\datasets0413_clean_rms.csv | CSV row count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_rows::descriptive_stats.csv | ASIS_ONLY | 7.0 | 05_results\descriptive_stats.csv | CSV row count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_rows::features_voc_0413.csv | ASIS_ONLY | 1000.0 | 03_features\features_voc_0413.csv | CSV row count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_rows::features_voc_0413_rms.csv | ASIS_ONLY | 1000.0 | 03_features\features_voc_0413_rms.csv | CSV row count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_rows::global_ref_rms.csv | ASIS_ONLY | 4.0 | 05_results\global_ref_rms.csv | CSV row count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_rows::leave_one_out.csv | ASIS_ONLY | 4.0 | 05_results\leave_one_out.csv | CSV row count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| csv_rows::sensitivity.csv | ASIS_ONLY | 7.0 | 05_results\sensitivity.csv | CSV row count | ASIS_ONLY | NOT_APPLICABLE | ASIS_ONLY | |||||||||
| ... 중간 263행은 전체 데이터 부록 ZIP에 원본으로 포함 ... | ||||||||||||||||
| csv_rows::voc_whisper_small_results_failed_rows.csv | TOBE_ONLY | 0.0 | results_by_count/n1000/snapshot/data\voc\stt\small_failed_backup_20260702_171422\voc_whisper_small_results_failed_rows.csv | CSV row count | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| csv_rows::voc_whisper_small_results_original_backup.csv | TOBE_ONLY | 12.0 | results_by_count/n1000/snapshot/data\voc\stt\small_failed_backup_20260702_171422\voc_whisper_small_results_original_backup.csv | CSV row count | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| csv_rows::weight_sensitivity.csv | TOBE_ONLY | 6.0 | results_by_count/n1000/snapshot/results\tables\weight_sensitivity.csv | CSV row count | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| csv_rows::why_why_why_structure.csv | TOBE_ONLY | 6.0 | results_by_count/n1000/snapshot/results\tables\meeting_0627\why_why_why_structure.csv | CSV row count | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| kspon_mean_cer | TOBE_ONLY | 1.0 | results_by_count/n1000/snapshot/results\tables\stt_reliability.csv | stt_reliability | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| kspon_mean_wer | TOBE_ONLY | 1.0 | results_by_count/n1000/snapshot/results\tables\stt_reliability.csv | stt_reliability | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| kspon_median_cer | TOBE_ONLY | 1.0 | results_by_count/n1000/snapshot/results\tables\stt_reliability.csv | stt_reliability | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| kspon_median_wer | TOBE_ONLY | 1.0 | results_by_count/n1000/snapshot/results\tables\stt_reliability.csv | stt_reliability | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| kspon_stt_n | TOBE_ONLY | 100.0 | results_by_count/n1000/snapshot/results\tables\stt_reliability.csv | stt_reliability | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| mean_cer_count | TOBE_ONLY | 1.0 | results_by_count/n1000/snapshot/results\tables\future_analysis\stt_wpm_reliability_summary.csv | raw numeric column | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| mean_cer_max | TOBE_ONLY | 0.2050518579102737 | results_by_count/n1000/snapshot/results\tables\future_analysis\stt_wpm_reliability_summary.csv | raw numeric column | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| mean_cer_mean | TOBE_ONLY | 0.2050518579102737 | results_by_count/n1000/snapshot/results\tables\future_analysis\stt_wpm_reliability_summary.csv | raw numeric column | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| mean_cer_min | TOBE_ONLY | 0.2050518579102737 | results_by_count/n1000/snapshot/results\tables\future_analysis\stt_wpm_reliability_summary.csv | raw numeric column | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| mean_cer_std | TOBE_ONLY | 0.0 | results_by_count/n1000/snapshot/results\tables\future_analysis\stt_wpm_reliability_summary.csv | raw numeric column | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| mean_wer_count | TOBE_ONLY | 1.0 | results_by_count/n1000/snapshot/results\tables\future_analysis\stt_wpm_reliability_summary.csv | raw numeric column | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| mean_wer_max | TOBE_ONLY | 0.3878978364810636 | results_by_count/n1000/snapshot/results\tables\future_analysis\stt_wpm_reliability_summary.csv | raw numeric column | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| mean_wer_mean | TOBE_ONLY | 0.3878978364810636 | results_by_count/n1000/snapshot/results\tables\future_analysis\stt_wpm_reliability_summary.csv | raw numeric column | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| mean_wer_min | TOBE_ONLY | 0.3878978364810636 | results_by_count/n1000/snapshot/results\tables\future_analysis\stt_wpm_reliability_summary.csv | raw numeric column | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| mean_wer_std | TOBE_ONLY | 0.0 | results_by_count/n1000/snapshot/results\tables\future_analysis\stt_wpm_reliability_summary.csv | raw numeric column | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| wer_mean | TOBE_ONLY | 1.0 | results_by_count/n1000/snapshot/results\stt_validation\kspon_wer_cer_by_file.csv | wer/cer file | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| wer_median | TOBE_ONLY | 1.0 | results_by_count/n1000/snapshot/results\stt_validation\kspon_wer_cer_by_file.csv | wer/cer file | selected_slot_snapshot:n1000 | TOBE_ONLY | NOT_APPLICABLE | TOBE_ONLY | |||||||||
| energy_mean_mean | RESOLVED_REVIEWED | 0.0679645110266973 | 0.0488152021351269 | -0.0191493088915703 | -0.2817545304496968 | 04_final\datasets0413_clean.csv | results_by_count/n1000/snapshot/results\stress_index\voc_stress_score_rebuilt.csv | raw numeric column | raw numeric column | selected_slot_snapshot:n1000 | RESOLVED_REVIEWED | NOT_APPLICABLE | RESOLVED_REVIEWED | ||||
| energy_mean_min | RESOLVED_REVIEWED | 0.0127306031063199 | 0.0072453501634299 | -0.00548525294289 | -0.43087141253873 | 04_final\datasets0413_clean.csv | results_by_count/n1000/snapshot/results\stress_index\voc_stress_score_rebuilt.csv | raw numeric column | raw numeric column | selected_slot_snapshot:n1000 | RESOLVED_REVIEWED | NOT_APPLICABLE | RESOLVED_REVIEWED | ||||
| energy_mean_std | RESOLVED_REVIEWED | 0.0292308259993892 | 0.0229951877097163 | -0.0062356382896729 | -0.2133240535112911 | 04_final\datasets0413_clean.csv | results_by_count/n1000/snapshot/results\stress_index\voc_stress_score_rebuilt.csv | raw numeric column | raw numeric column | selected_slot_snapshot:n1000 | RESOLVED_REVIEWED | NOT_APPLICABLE | RESOLVED_REVIEWED | ||||
| stt_transcript_empty_rate | RESOLVED_REVIEWED | 0.003 | 0.0 | -0.003 | -1.0 | stt_1000.csv | results_by_count/n1000/snapshot/data\voc\stt\voc_whisper_small_results.csv | column=text | column=transcript_path | selected_slot_snapshot:n1000 | RESOLVED_REVIEWED | NOT_APPLICABLE | RESOLVED_REVIEWED |
산출물 JSON: results/tables/asis_tobe/asis_tobe_compare_manifest.json
전체 행 포함
| 경로 | 값 |
|---|---|
| generated_at | 2026-07-04 18:35:55 |
| asis_dir | C:\jupyter_env\datasets0413 |
| tobe_dir | C:\AI\sci_voc_bot |
| tobe_profile | n1000 |
| selected_snapshot | C:\AI\sci_voc_bot\results_by_count\n1000\snapshot |
| slot_snapshot_found | True |
| using_root_fallback | False |
| stale_snapshot_detected | False |
| snapshot_stt_count | 994 |
| root_stt_count | 994 |
| source_selection_note | selected snapshot used |
| outputs.file_inventory_all.csv | C:\AI\sci_voc_bot\results\tables\asis_tobe\file_inventory_all.csv |
| outputs.folder_extension_summary.csv | C:\AI\sci_voc_bot\results\tables\asis_tobe\folder_extension_summary.csv |
| outputs.file_inventory_compare.csv | C:\AI\sci_voc_bot\results\tables\asis_tobe\file_inventory_compare.csv |
| outputs.csv_profile_all.csv | C:\AI\sci_voc_bot\results\tables\asis_tobe\csv_profile_all.csv |
| outputs.csv_schema_compare.csv | C:\AI\sci_voc_bot\results\tables\asis_tobe\csv_schema_compare.csv |
| outputs.key_metrics_all.csv | C:\AI\sci_voc_bot\results\tables\asis_tobe\key_metrics_all.csv |
| outputs.tobe_metric_source_trace.csv | C:\AI\sci_voc_bot\results\tables\asis_tobe\tobe_metric_source_trace.csv |
| outputs.key_metric_compare.csv | C:\AI\sci_voc_bot\results\tables\asis_tobe\key_metric_compare.csv |
| outputs.difference_root_cause_analysis.csv | C:\AI\sci_voc_bot\results\tables\asis_tobe\difference_root_cause_analysis.csv |
| outputs.difference_action_plan.csv | C:\AI\sci_voc_bot\results\tables\asis_tobe\difference_action_plan.csv |
| outputs.difference_problem_trace.csv | C:\AI\sci_voc_bot\results\tables\asis_tobe\difference_problem_trace.csv |
| outputs.asis_tobe_comparison_summary_ko.md | C:\AI\sci_voc_bot\results\manuscript_text\asis_tobe\asis_tobe_comparison_summary_ko.md |
| outputs.asis_tobe_comparison_report_ko.docx | C:\AI\sci_voc_bot\reports\asis_tobe\asis_tobe_comparison_report_ko.docx |
| outputs.asis_tobe_comparison_report_en.docx | C:\AI\sci_voc_bot\reports\asis_tobe\asis_tobe_comparison_report_en.docx |
산출물 CSV: results/tables/asis_tobe/csv_schema_compare.csv (행 138, 열 17)
전체 행 포함
| pair_key | filename | as_is_path | as_is_rows | as_is_cols | as_is_columns | as_is_status | to_be_path | to_be_rows | to_be_cols | to_be_columns | to_be_status | status | original_status | professor_ready_status | resolution_status | resolution_note |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| anova.csv | anova.csv | 05_results\anova.csv | 4.0 | 4.0 | variable; F; p_value; eta_squared | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| correlation_4vars.csv | correlation_4vars.csv | 05_results\correlation_4vars.csv | 4.0 | 5.0 | metric; x; value; p_value; n | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| datasets0413_clean.csv | datasets0413_clean.csv | 04_final\datasets0413_clean.csv | 998.0 | 13.0 | wav_id; energy_mean; pitch_mean; duration_sec; word_cnt; wpm; text_len; z_energy_mean; z_pitch_mean; z_wpm; z_duration_sec; raw_stress; stress_score | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| datasets0413_clean_rms.csv | datasets0413_clean_rms.csv | 04_final\datasets0413_clean_rms.csv | 998.0 | 13.0 | wav_id; energy_mean; pitch_mean; duration_sec; word_cnt; text_len; wpm; z_energy_mean; z_pitch_mean; z_wpm; z_duration_sec; raw_stress; stress_score | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| descriptive_stats.csv | descriptive_stats.csv | 05_results\descriptive_stats.csv | 7.0 | 9.0 | Unnamed: 0; count; mean; std; min; 25%; 50%; 75%; max | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| features_voc_0413.csv | features_voc_0413.csv | 03_features\features_voc_0413.csv | 1000.0 | 4.0 | wav_id; energy_mean; pitch_mean; duration_sec | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| features_voc_0413_rms.csv | features_voc_0413_rms.csv | 03_features\features_voc_0413_rms.csv | 1000.0 | 4.0 | wav_id; energy_mean; pitch_mean; duration_sec | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| global_ref_rms.csv | global_ref_rms.csv | 05_results\global_ref_rms.csv | 4.0 | 7.0 | variable; mu; sigma; min; max; method; source | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| leave_one_out.csv | leave_one_out.csv | 05_results\leave_one_out.csv | 4.0 | 4.0 | dropped_variable; corr_with_full; p_value; mean_abs_diff | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| sensitivity.csv | sensitivity.csv | 05_results\sensitivity.csv | 7.0 | 4.0 | scenario; corr_with_base; p_value; mean_abs_diff | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| stt_1000.csv | stt_1000.csv | stt_1000.csv | 1000.0 | 7.0 | wav_id; text; word_cnt; text_len; source; stt_ok; sample_order | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| tukey.csv | tukey.csv | 05_results\tukey.csv | 12.0 | 8.0 | variable; group1; group2; meandiff; p_adj; lower; upper; reject | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| validity_summary.csv | validity_summary.csv | 05_results\validity_summary.csv | 3.0 | 2.0 | item; value | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | ||||||
| discriminant_details.csv | discriminant_details.csv | 05_results\discriminant_details.csv | 4.0 | 4.0 | variable; r; p_value; abs_r | ok | results_by_count/n1000/snapshot/results\tables\discriminant_details.csv | 4.0 | 4.0 | variable; r; p_value; abs_r | ok | MATCH | MATCH | MATCH | NOT_APPLICABLE | |
| discriminant_details.csv | discriminant_details.csv | 05_results\discriminant_details.csv | 4.0 | 4.0 | variable; r; p_value; abs_r | ok | results_by_count/n1000/snapshot/results\validity\discriminant_details.csv | 4.0 | 4.0 | variable; r; p_value; abs_r | ok | MATCH | MATCH | MATCH | NOT_APPLICABLE | |
| bootstrap_ci.csv | bootstrap_ci.csv | 05_results\bootstrap_ci.csv | 4.0 | 5.0 | variable; boot_mean_r; ci_low_2_5; ci_high_97_5; n_boot | ok | results_by_count/n1000/snapshot/results\tables\bootstrap_ci.csv | 4.0 | 5.0 | variable; r; ci_lower; ci_upper; n_boot | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |
| bootstrap_ci.csv | bootstrap_ci.csv | 05_results\bootstrap_ci.csv | 4.0 | 5.0 | variable; boot_mean_r; ci_low_2_5; ci_high_97_5; n_boot | ok | results_by_count/n1000/snapshot/results\validity\bootstrap_ci.csv | 4.0 | 5.0 | variable; r; ci_lower; ci_upper; n_boot | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |
| correlation_matrix.csv | correlation_matrix.csv | 05_results\correlation_matrix.csv | 7.0 | 8.0 | Unnamed: 0; energy_mean; pitch_mean; wpm; duration_sec; stress_score; word_cnt; text_len | ok | results_by_count/n1000/snapshot/results\tables\correlation_matrix.csv | 7.0 | 8.0 | variable; stress_score; energy_mean; pitch_mean; wpm; duration_sec; word_cnt; text_len | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |
| correlation_matrix.csv | correlation_matrix.csv | 05_results\correlation_matrix.csv | 7.0 | 8.0 | Unnamed: 0; energy_mean; pitch_mean; wpm; duration_sec; stress_score; word_cnt; text_len | ok | results_by_count/n1000/snapshot/results\validity\correlation_matrix.csv | 7.0 | 8.0 | variable; stress_score; energy_mean; pitch_mean; wpm; duration_sec; word_cnt; text_len | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |
| multivariate_ols.csv | multivariate_ols.csv | 05_results\multivariate_ols.csv | 5.0 | 8.0 | term; coef; std_err; t; p_value; r2; adj_r2; n | ok | results_by_count/n1000/snapshot/results\tables\multivariate_ols.csv | 4.0 | 9.0 | term; coef; std_err; t; p_value; r2; adj_r2; n; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |
| multivariate_ols.csv | multivariate_ols.csv | 05_results\multivariate_ols.csv | 5.0 | 8.0 | term; coef; std_err; t; p_value; r2; adj_r2; n | ok | results_by_count/n1000/snapshot/results\validity\multivariate_ols.csv | 4.0 | 9.0 | term; coef; std_err; t; p_value; r2; adj_r2; n; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |
| univariate_ols.csv | univariate_ols.csv | 05_results\univariate_ols.csv | 4.0 | 7.0 | model; intercept; slope; r2; t_slope; p_slope; n | ok | results_by_count/n1000/snapshot/results\tables\univariate_ols.csv | 4.0 | 8.0 | model; intercept; slope; r2; t_slope; p_slope; n; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |
| univariate_ols.csv | univariate_ols.csv | 05_results\univariate_ols.csv | 4.0 | 7.0 | model; intercept; slope; r2; t_slope; p_slope; n | ok | results_by_count/n1000/snapshot/results\validity\univariate_ols.csv | 4.0 | 8.0 | model; intercept; slope; r2; t_slope; p_slope; n; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |
| vif.csv | vif.csv | 05_results\vif.csv | 4.0 | 2.0 | variable; VIF | ok | results_by_count/n1000/snapshot/results\tables\vif.csv | 4.0 | 3.0 | variable; VIF; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |
| vif.csv | vif.csv | 05_results\vif.csv | 4.0 | 2.0 | variable; VIF | ok | results_by_count/n1000/snapshot/results\validity\vif.csv | 4.0 | 3.0 | variable; VIF; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |
| additional_research_execution_order.csv | results_by_count/n1000/snapshot/results\tables\future_analysis\additional_research_execution_order.csv | 6.0 | 5.0 | order; step; menu; action; required_now | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| additional_research_plan.csv | results_by_count/n1000/snapshot/results\tables\future_analysis\additional_research_plan.csv | 5.0 | 9.0 | stage; topic; current_limitation; required_data; recommended_sample; method; expected_output; paper_usage; priority | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| additional_research_required_data.csv | results_by_count/n1000/snapshot/results\tables\future_analysis\additional_research_required_data.csv | 5.0 | 4.0 | data_item; columns; needed_for; current_template | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| additional_research_status.csv | results_by_count/n1000/snapshot/results\tables\future_analysis\additional_research_status.csv | 1.0 | 5.0 | task; status; stress_score_n; stt_reliability_available; message | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| additional_research_why_method_direction.csv | results_by_count/n1000/snapshot/results\tables\meeting_0627\additional_research_why_method_direction.csv | 5.0 | 5.0 | 주제; 왜 필요한가; 왜 이 기법인가; 방향성; 이번 보고에서의 표현 | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| aiis_branch_overview.csv | results_by_count/n1000/snapshot/results\tables\aiis_branch_overview.csv | 2.0 | 5.0 | branch_name; branch_dir; manifest_exists; status_exists; updated_at | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| aiis_final_diff_actions.csv | results_by_count/n1000/snapshot/results\tables\aiis_final_diff_actions.csv | 2.0 | 4.0 | priority; action; command_or_screen; done_when | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| aiis_final_diff_metrics.csv | results_by_count/n1000/snapshot/results\tables\aiis_final_diff_metrics.csv | 10.0 | 8.0 | metric; asis_value; tobe_value; delta; unit; status; reason; source | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| aiis_final_diff_overview.csv | results_by_count/n1000/snapshot/results\tables\aiis_final_diff_overview.csv | 9.0 | 3.0 | item; value; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| aiis_final_diff_root_causes.csv | results_by_count/n1000/snapshot/results\tables\aiis_final_diff_root_causes.csv | 5.0 | 7.0 | priority; level; title; evidence; why_it_happened; paper_impact; next_action | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| aiis_key_metric_compare.csv | results_by_count/n1000/snapshot/results\tables\aiis_key_metric_compare.csv | 6.0 | 8.0 | metric; asis_value; tobe_value; delta; asis_source; tobe_source; status; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| aiis_recommended_actions.csv | results_by_count/n1000/snapshot/results\tables\aiis_recommended_actions.csv | 1.0 | 7.0 | order; action_code; action_name; purpose; command_hint; dashboard_path; expected_result | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| aiis_root_causes.csv | results_by_count/n1000/snapshot/results\tables\aiis_root_causes.csv | 1.0 | 10.0 | cause_code; severity; title; problem; why; evidence; paper_impact; check_next; recommended_action_ref; source | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| aiis_snapshot_status.csv | results_by_count/n1000/snapshot/results\tables\aiis_snapshot_status.csv | 1.0 | 12.0 | slot; slot_snapshot_found; tobe_scan_roots; aiis_extension_status; core_message; asis_stt_n; tobe_stt_n; asis_feature_n; tobe_feature_n; asis_stress_n; tobe_stress_n; valid_tobe_stt_source | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| aiis_tracking_metrics.csv | results_by_count/n1000/snapshot/results\tables\aiis_tracking_metrics.csv | 4.0 | 5.0 | tracking_metric; target; current_value; status; source | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| anova_group_descriptive.csv | results_by_count/n1000/snapshot/results\tables\anova_group_descriptive.csv | 3.0 | 16.0 | stress_group; stress_score_count; stress_score_mean; stress_score_std; energy_mean_count; energy_mean_mean; energy_mean_std; pitch_mean_count; pitch_mean_mean; pitch_mean_std; wpm_count; wpm_mean; wpm_std; duration_sec_count; duration_sec_mean; duration_sec_std | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| anova_profile.csv | results_by_count/n1000/snapshot/results\tables\anova_profile.csv | 4.0 | 7.0 | variable; F; p_value; eta_squared; effect_size; note; p_value_display | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| anova_profile.csv | results_by_count/n1000/snapshot/results\validity\anova_profile.csv | 4.0 | 7.0 | variable; F; p_value; eta_squared; effect_size; note; p_value_display | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| applied_sciences_required_sections.csv | results_by_count/n1000/snapshot/results\tables\applied_sciences_required_sections.csv | 20.0 | 3.0 | section_or_statement; source_or_field; status | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| artifact_lineage.csv | results_by_count/n1000/snapshot/results\tables\repro\artifact_lineage.csv | 9.0 | 8.0 | artifact; depends_on; source_stage; upstream_1; upstream_2; upstream_3; upstream_4; risk_if_changed | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| auto_execution_plan.csv | results_by_count/n1000/snapshot/results\tables\repro\auto_execution_plan.csv | 2.0 | 10.0 | run_order; issue; status; severity; evidence; likely_cause; recommended_action; menu_no; command_hint; priority | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| claim_strength_check.csv | results_by_count/n1000/snapshot/results\tables\ops\claim_strength_check.csv | 10.0 | 5.0 | risk_phrase; recommended_expression; found_count; files; decision | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| complete_report_checklist.csv | results_by_count/n1000/snapshot/results\tables\complete_report_checklist.csv | 34.0 | 10.0 | order; group; title; kind; required; exists; rows_or_note; path; status; description | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| construct_validity_summary.csv | results_by_count/n1000/snapshot/results\tables\construct_validity_summary.csv | 7.0 | 3.0 | indicator; value; decision | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| correlation_validity.csv | results_by_count/n1000/snapshot/results\tables\correlation_validity.csv | 4.0 | 6.0 | variable; r; p_value; validity_type; decision; p_value_display | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| criterion_validity_status.csv | results_by_count/n1000/snapshot/results\tables\future_analysis\criterion_validity_status.csv | 1.0 | 4.0 | task; status; message; required_file | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| csv_profile_all.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\csv_profile_all.csv | 144.0 | 9.0 | side; relative_path; filename; stem; rows; cols; columns; status; scan_scope | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| csv_schema_compare.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\csv_schema_compare.csv | 138.0 | 13.0 | pair_key; filename; as_is_path; as_is_rows; as_is_cols; as_is_columns; as_is_status; to_be_path; to_be_rows; to_be_cols; to_be_columns; to_be_status; status | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| data_quality_action_plan.csv | results_by_count/n1000/snapshot/results\tables\data_quality_action_plan.csv | 3.0 | 7.0 | priority; action_code; action_name; trigger_items; reason; auto_executable; command_hint | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| data_quality_status.csv | results_by_count/n1000/snapshot/results\tables\data_quality_status.csv | 9.0 | 7.0 | check_item; status; value; criterion; message; recommended_action; severity | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| deepseek_learning_feedback.csv | results_by_count/n1000/snapshot/results\tables\learning\deepseek_learning_feedback.csv | 5.0 | 3.0 | area; status; recommendation | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| deploy_result_integrity.csv | results_by_count/n1000/snapshot/results\tables\deploy_result_integrity.csv | 22.0 | 7.0 | artifact; source_type; path; exists; rows_or_exists; mtime; size_kb | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| descriptive_statistics.csv | results_by_count/n1000/snapshot/results\tables\descriptive_statistics.csv | 5.0 | 9.0 | variable; count; mean; std; min; 25%; 50%; 75%; max | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| diagnosis.csv | results_by_count/n1000/snapshot/results\tables\repro\diagnosis.csv | 4.0 | 8.0 | issue; status; severity; evidence; likely_cause; recommended_action; menu_no; command_hint | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| difference_action_plan.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\difference_action_plan.csv | 2.0 | 5.0 | priority; action; why; evidence; target_menu | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| difference_problem_trace.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\difference_problem_trace.csv | 19.0 | 6.0 | metric_key; status; as_is_value; to_be_value; as_is_source; to_be_source | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| difference_root_cause_analysis.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\difference_root_cause_analysis.csv | 2.0 | 8.0 | priority; domain; severity; finding; evidence; likely_cause; next_check; recommended_action | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| failed_file_inventory.csv | results_by_count/n1000/snapshot/results\tables\ops\failed_file_inventory.csv | 600.0 | 9.0 | profile; dataset; source_csv; file_id; file; status; error; ok; transcript_len | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| feature_descriptive_statistics.csv | results_by_count/n1000/snapshot/results\tables\feature_descriptive_statistics.csv | 4.0 | 9.0 | variable; count; mean; std; min; 25%; 50%; 75%; max | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| file_inventory_all.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\file_inventory_all.csv | 2427.0 | 11.0 | side; root; relative_path; filename; stem; extension; size_bytes; modified_at; sha256; status; scan_scope | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| file_inventory_compare.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\file_inventory_compare.csv | 2421.0 | 12.0 | filename; as_is_path; extension; as_is_size; as_is_modified; as_is_sha256; to_be_path; to_be_size; to_be_modified; to_be_sha256; status; size_diff | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| final_dataset_decision_matrix.csv | results_by_count/n1000/snapshot/results\tables\scie_submission\final_dataset_decision_matrix.csv | 3.0 | 7.0 | option; role; dataset_policy; strength; risk; recommended_use; decision | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| final_paper_quality_check.csv | results_by_count/n1000/snapshot/results\tables\final_paper_quality_check.csv | 53.0 | 6.0 | section_id; section_title; issue_type; matched_text; severity; suggested_fix | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| final_submission_board.csv | results_by_count/n1000/snapshot/results\tables\repro\final_submission_board.csv | 6.0 | 4.0 | category; status; item; detail | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| folder_extension_summary.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\folder_extension_summary.csv | 20.0 | 5.0 | side; top_folder; extension; file_count; total_size_bytes | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| future_research_action_plan.csv | results_by_count/n1000/snapshot/results\tables\future_research_action_plan.csv | 4.0 | 7.0 | future_task; related_limitation; priority; recommended_sample; method; statistics; paper_usage | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| future_research_limitations.csv | results_by_count/n1000/snapshot/results\tables\future_research_limitations.csv | 4.0 | 8.0 | id; title_ko; current_limitation; future_analysis; future_outputs; paper_position; current_data_status; future_data_needed | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| journal_target_strategy.csv | results_by_count/n1000/snapshot/results\tables\scie_submission\journal_target_strategy.csv | 3.0 | 5.0 | tier; journal_candidate; positioning; needed_before_submit; risk | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| key_metric_compare.csv | results_by_count/n1000/snapshot/results\tables\key_metric_compare.csv | 6.0 | 8.0 | metric; asis_value; tobe_value; delta; asis_source; tobe_source; status; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| key_metric_compare.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\key_metric_compare.csv | 313.0 | 10.0 | metric_key; status; as_is_value; to_be_value; diff_to_be_minus_as_is; pct_diff; as_is_source; to_be_source; as_is_note; to_be_note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| key_metrics_all.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\key_metrics_all.csv | 370.0 | 5.0 | side; metric_key; metric_value; source_file; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| kspon_manifest.csv | results_by_count/n1000/snapshot/data\kspon\manifest\kspon_manifest.csv | 100.0 | 12.0 | file_id; pcm_source_path; txt_source_path; pcm_target_path; txt_target_path; has_audio; has_transcript; file_size_kb; source_folder; dataset; audio_copied; txt_copied | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| kspon_validation_plan.csv | results_by_count/n1000/snapshot/results\tables\scie_submission\kspon_validation_plan.csv | 5.0 | 5.0 | step; minimum; recommended; output; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| kspon_wer_cer_by_file.csv | results_by_count/n1000/snapshot/results\stt_validation\kspon_wer_cer_by_file.csv | 100.0 | 8.0 | file_id; reference_path; has_reference; reference_text; hypothesis_text; wer; cer; model | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| kspon_wer_cer_summary.csv | results_by_count/n1000/snapshot/results\stt_validation\kspon_wer_cer_summary.csv | 1.0 | 9.0 | model; dataset; n; mean_wer; median_wer; mean_cer; median_cer; status; message | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| kspon_whisper_medium_results.csv | results_by_count/n1000/snapshot/data\kspon\stt\kspon_whisper_medium_results.csv | 100.0 | 14.0 | file_id; audio_path; transcript_path; transcript; model; language; device; compute_type; beam_size; vad_filter; audio_duration_sec; ok; error; elapsed_sec | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| learning_dashboard.csv | results_by_count/n1000/snapshot/results\tables\learning\learning_dashboard.csv | 4.0 | 3.0 | item; value; interpretation | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| learning_progress_score.csv | results_by_count/n1000/snapshot/results\tables\learning\learning_progress_score.csv | 1.0 | 20.0 | generated_at; profile; learning_score; status; reasons; stress_score_n; stress_score_mean; voc_stt_n; voc_stt_nonempty; wpm_nonzero_rows; feature_rows; reference_count; has_final_board; has_three_way_compare; has_claim_check; has_submission_gate; has_security_check; source_stress; source_stt; source_features | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| loo_stability.csv | results_by_count/n1000/snapshot/results\tables\loo_stability.csv | 4.0 | 7.0 | removed_variable; remaining_variables; r; r_squared; p_value; decision; p_value_display | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| loo_stability.csv | results_by_count/n1000/snapshot/results\validity\loo_stability.csv | 4.0 | 7.0 | removed_variable; remaining_variables; r; r_squared; p_value; decision; p_value_display | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| meeting_0627_current_status.csv | results_by_count/n1000/snapshot/results\tables\meeting_0627\meeting_0627_current_status.csv | 1.0 | 17.0 | profile; snapshot_found; voc_stt_n; features_n; stress_n; kspon_stt_n; wpm_nonzero; wpm_mean; quality_fail; quality_warn; corr_energy; corr_pitch; corr_wpm; corr_duration; wer; cer; generated_at | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| monitoring_summary_n1000.csv | results_by_count/n1000/snapshot/results\tables\monitoring\monitoring_summary_n1000.csv | 13.0 | 3.0 | category; metric; value | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| multicenter_generalization_status.csv | results_by_count/n1000/snapshot/results\tables\future_analysis\multicenter_generalization_status.csv | 1.0 | 4.0 | task; status; message; required_file | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| next_action_plan.csv | results_by_count/n1000/snapshot/results\tables\repro\next_action_plan.csv | 2.0 | 10.0 | run_order; issue; status; severity; evidence; likely_cause; recommended_action; menu_no; command_hint; priority | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| outlier_robustness.csv | results_by_count/n1000/snapshot/results\robustness\outlier_robustness.csv | 4.0 | 8.0 | scenario; n; energy_mean_r; pitch_mean_r; wpm_r; duration_sec_r; min_weight_sensitivity_r; decision | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| outlier_robustness.csv | results_by_count/n1000/snapshot/results\tables\outlier_robustness.csv | 4.0 | 8.0 | scenario; n; energy_mean_r; pitch_mean_r; wpm_r; duration_sec_r; min_weight_sensitivity_r; decision | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| professor_questions_0627.csv | results_by_count/n1000/snapshot/results\tables\meeting_0627\professor_questions_0627.csv | 5.0 | 2.0 | 교수님 확인 질문; 왜 필요한가 | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| reference_papers_manifest.csv | results_by_count/n1000/snapshot/results\tables\reference_papers_manifest.csv | 0.0 | 1.0 |  | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| references.csv | results_by_count/n1000/snapshot/results\tables\references.csv | 30.0 | 10.0 | no; authors; year; title; source; volume; pages; doi; type; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| research_improvement_cases_dataset_manifest.csv | results_by_count/n1000/snapshot/results\tables\learning\research_improvement_cases_dataset_manifest.csv | 2.0 | 3.0 | task; source; quality | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| result_similarity_matrix.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\result_similarity_matrix.csv | 313.0 | 9.0 | metric_key; category; as_is_value; to_be_value; abs_diff; pct_diff; similarity_status; judgement_reason; source_note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| resume_steps_n1000.csv | results_by_count/n1000/snapshot/results\tables\resume\resume_steps_n1000.csv | 6.0 | 6.0 | step_no; step; count; target; status; command | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| reviewer_qa_dataset_manifest.csv | results_by_count/n1000/snapshot/results\tables\learning\reviewer_qa_dataset_manifest.csv | 0.0 | 1.0 |  | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| sample_match_details.csv | results_by_count/n1000/snapshot/results\tables\repro\sample_match_details.csv | 3000.0 | 2.0 | sample_id; status | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| sample_match_summary.csv | results_by_count/n1000/snapshot/results\tables\repro\sample_match_summary.csv | 5.0 | 3.0 | set_name; csv_file; id_count | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| scie_current_metric_snapshot.csv | results_by_count/n1000/snapshot/results\tables\scie_submission\scie_current_metric_snapshot.csv | 1.0 | 16.0 | profile; snapshot_path; snapshot_exists; stt_path; stt_rows; stt_success_unique; stt_failed_or_empty; stt_model_detected; feature_path; feature_n; stress_path; stress_n; wpm_mean; stress_mean; generated_at; analysis_policy | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| scie_readiness_gates.csv | results_by_count/n1000/snapshot/results\tables\scie_submission\scie_readiness_gates.csv | 7.0 | 5.0 | gate; requirement; current_status; decision; action | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| scie_reproducibility_checklist.csv | results_by_count/n1000/snapshot/results\tables\scie_submission\scie_reproducibility_checklist.csv | 6.0 | 5.0 | category; item; status; evidence; owner_note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| speaker_normalization_status.csv | results_by_count/n1000/snapshot/results\tables\future_analysis\speaker_normalization_status.csv | 1.0 | 4.0 | task; status; message; required_file | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| statistical_validation_checklist.csv | results_by_count/n1000/snapshot/results\tables\scie_submission\statistical_validation_checklist.csv | 7.0 | 4.0 | analysis; needed_for_scie; status; where_to_place | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| step_status_n1000.csv | results_by_count/n1000/snapshot/results\tables\history\step_status_n1000.csv | 113.0 | 15.0 | profile; section; step_no; step_label; status; status_rank; run_count; success_count; fail_count; last_event_time; last_status; last_return_code; last_elapsed_sec; last_command; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| stress_index_feature_quality.csv | results_by_count/n1000/snapshot/results\tables\stress_index_feature_quality.csv | 4.0 | 6.0 | variable; valid_n; unique_n; mean; std_ddof0; status | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| stt_error_sensitivity.csv | results_by_count/n1000/snapshot/results\robustness\stt_error_sensitivity.csv | 6.0 | 8.0 | scenario; wpm_multiplier; correlation_with_baseline; p_value; p_value_display; mad; decision; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| stt_error_sensitivity.csv | results_by_count/n1000/snapshot/results\tables\stt_error_sensitivity.csv | 6.0 | 8.0 | scenario; wpm_multiplier; correlation_with_baseline; p_value; p_value_display; mad; decision; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| stt_reliability.csv | results_by_count/n1000/snapshot/results\tables\stt_reliability.csv | 1.0 | 9.0 | model; dataset; n; mean_wer; median_wer; mean_cer; median_cer; status; message | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| stt_wpm_reliability_sources.csv | results_by_count/n1000/snapshot/results\tables\future_analysis\stt_wpm_reliability_sources.csv | 3.0 | 3.0 | source; path; status | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| stt_wpm_reliability_status.csv | results_by_count/n1000/snapshot/results\tables\future_analysis\stt_wpm_reliability_status.csv | 1.0 | 4.0 | task; status; message; required_file | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| stt_wpm_reliability_summary.csv | results_by_count/n1000/snapshot/results\tables\future_analysis\stt_wpm_reliability_summary.csv | 1.0 | 7.0 | model; dataset; n; mean_wer; median_wer; mean_cer; median_cer | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| submission_readiness_check.csv | results_by_count/n1000/snapshot/results\tables\submission_readiness_check.csv | 19.0 | 4.0 | check; value; status; required_fix | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| submission_readiness_checklist.csv | results_by_count/n1000/snapshot/results\tables\submission_readiness_checklist.csv | 15.0 | 5.0 | item; status; severity; message; recommended_action | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| submitted_doc_numeric_contexts.csv | results_by_count/n1000/snapshot/results\tables\repro\submitted_doc_numeric_contexts.csv | 29.0 | 5.0 | docx_file; context_id; tag; value; context | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| three_way_compare.csv | results_by_count/n1000/snapshot/results\tables\repro\three_way_compare.csv | 19.0 | 15.0 | metric_key; status; document_value_candidate; as_is_value; to_be_value; to_be_minus_as_is; document_minus_tobe; docx_file; tobe_root; snapshot_found; root_fallback; stale_snapshot_detected; snapshot_stt_count; root_stt_count; interpretation | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| tobe_metric_source_trace.csv | results_by_count/n1000/snapshot/results\tables\asis_tobe\tobe_metric_source_trace.csv | 283.0 | 5.0 | side; metric_key; metric_value; source_file; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| training_examples_by_task.csv | results_by_count/n1000/snapshot/results\tables\learning\training_examples_by_task.csv | 0.0 | 2.0 | task; example_count | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| training_examples_manifest.csv | results_by_count/n1000/snapshot/results\tables\learning\training_examples_manifest.csv | 0.0 | 1.0 |  | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| tukey_hsd.csv | results_by_count/n1000/snapshot/results\tables\tukey_hsd.csv | 12.0 | 9.0 | variable; group1; group2; meandiff; p_adj; lower; upper; reject; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| tukey_hsd.csv | results_by_count/n1000/snapshot/results\validity\tukey_hsd.csv | 12.0 | 9.0 | variable; group1; group2; meandiff; p_adj; lower; upper; reject; note | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| validity_structure_summary.csv | results_by_count/n1000/snapshot/results\tables\validity_structure_summary.csv | 1.0 | 3.0 | convergent_mean_abs_r; auxiliary_mean_abs_r; delta | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_features.csv | results_by_count/n1000/snapshot/data\voc\features\voc_features.csv | 1000.0 | 12.0 | wav_id; audio_path; energy_mean; pitch_mean; duration_sec; transcript; word_cnt; text_len; wpm; stt_status; ok; error | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_manifest.csv | results_by_count/n1000/snapshot/data\voc\manifest\voc_manifest.csv | 1000.0 | 8.0 | file_id; source_path; target_path; file_name; extension; file_size_kb; dataset; copied | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_stress_score_rebuilt.csv | results_by_count/n1000/snapshot/results\stress_index\voc_stress_score_rebuilt.csv | 1000.0 | 18.0 | wav_id; audio_path; energy_mean; pitch_mean; duration_sec; transcript; word_cnt; text_len; wpm; stt_status; ok; error; z_energy_mean; z_pitch_mean; z_wpm; z_duration_sec; stress_raw; stress_score | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_stt_completeness.csv | results_by_count/n1000/snapshot/results\tables\voc_stt_completeness.csv | 1.0 | 6.0 | n; transcript_nonempty; word_cnt_nonzero; wpm_nonzero; stt_missing_rows; status | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_whisper_medium_results.csv | results_by_count/n1000/snapshot/data\voc\stt\voc_whisper_medium_results.csv | 8.0 | 14.0 | file_id; audio_path; transcript_path; transcript; model; language; device; compute_type; beam_size; vad_filter; audio_duration_sec; ok; error; elapsed_sec | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_whisper_small_results.csv | results_by_count/n1000/snapshot/data\voc\stt\voc_whisper_small_results.csv | 1000.0 | 14.0 | file_id; audio_path; transcript_path; transcript; model; language; device; compute_type; beam_size; vad_filter; audio_duration_sec; ok; error; elapsed_sec | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_whisper_small_results_failed_rows.csv | results_by_count/n1000/snapshot/data\voc\stt\small_failed_backup_20260702_171422\voc_whisper_small_results_failed_rows.csv | 0.0 | 1.0 |  | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_whisper_small_results_failed_rows.csv | results_by_count/n1000/snapshot/data\voc\stt\small_failed_backup_20260702_171446\voc_whisper_small_results_failed_rows.csv | 0.0 | 1.0 |  | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_whisper_small_results_failed_rows.csv | results_by_count/n1000/snapshot/data\voc\stt\small_failed_backup_20260702_173438\voc_whisper_small_results_failed_rows.csv | 1.0 | 14.0 | file_id; audio_path; transcript_path; transcript; model; language; device; compute_type; beam_size; vad_filter; audio_duration_sec; ok; error; elapsed_sec | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_whisper_small_results_original_backup.csv | results_by_count/n1000/snapshot/data\voc\stt\small_failed_backup_20260702_171422\voc_whisper_small_results_original_backup.csv | 12.0 | 14.0 | file_id; audio_path; transcript_path; transcript; model; language; device; compute_type; beam_size; vad_filter; audio_duration_sec; ok; error; elapsed_sec | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_whisper_small_results_original_backup.csv | results_by_count/n1000/snapshot/data\voc\stt\small_failed_backup_20260702_171446\voc_whisper_small_results_original_backup.csv | 12.0 | 14.0 | file_id; audio_path; transcript_path; transcript; model; language; device; compute_type; beam_size; vad_filter; audio_duration_sec; ok; error; elapsed_sec | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| voc_whisper_small_results_original_backup.csv | results_by_count/n1000/snapshot/data\voc\stt\small_failed_backup_20260702_173438\voc_whisper_small_results_original_backup.csv | 100.0 | 14.0 | file_id; audio_path; transcript_path; transcript; model; language; device; compute_type; beam_size; vad_filter; audio_duration_sec; ok; error; elapsed_sec | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| weight_sensitivity.csv | results_by_count/n1000/snapshot/results\tables\weight_sensitivity.csv | 6.0 | 7.0 | scenario; weights_e_p_w_d; correlation_with_baseline; p_value; mad; decision; p_value_display | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| weight_sensitivity.csv | results_by_count/n1000/snapshot/results\validity\weight_sensitivity.csv | 6.0 | 7.0 | scenario; weights_e_p_w_d; correlation_with_baseline; p_value; mad; decision; p_value_display | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE | |||||||
| why_why_why_structure.csv | results_by_count/n1000/snapshot/results\tables\meeting_0627\why_why_why_structure.csv | 6.0 | 2.0 | 질문; 답변 | ok | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | RESOLVED_SCHEMA_NORMALIZED | NOT_APPLICABLE |
산출물 텍스트: research_continuity/11_asis_tobe_gap_v163/asis_tobe_gap_diagnosis_v163.md
# AS-IS/TO-BE stress_score 평균 차이 진단 v163 - AS-IS mean: 0.517904 - TO-BE mean: 0.636488 - 차이: +0.118584 - 판정: GAP_DIAGNOSIS_REQUIRED__NOT_MP3_ONLY_YET > AS-IS와 TO-BE의 stress_score 평균 차이가 남아 있어 TO-BE가 AS-IS를 완전 재현했다고 보지는 않았습니다. 다만 동일 wav_id 교집합, 0413 고정 산식, 동일 WPM 기준으로 AS-IS-compatible 값을 별도 산출해 차이 원인을 정규화·특징추출·STT/WPM·표본 차이로 분해하겠습니다.
산출물 텍스트: results/manuscript_text/asis_tobe/asis_tobe_comparison_summary_ko.md
# AS-IS / TO-BE 전체 비교 분석 요약 - 생성 시각: 2026-07-04 18:35:55 - AS-IS 경로: `C:\jupyter_env\datasets0413` - TO-BE 경로: `C:\AI\sci_voc_bot` - TO-BE 선택 슬롯: `n1000` - TO-BE 실제 비교 기준: `선택 슬롯 snapshot` - TO-BE snapshot 경로: `C:\AI\sci_voc_bot\results_by_count\n1000\snapshot` ## 1. 핵심 판단 - 판정: 자동 비교 기준에서는 치명 차이가 제한적입니다. 다만 REVIEW/DIFF 항목은 수동 확인이 필요합니다. ## 2. 차이 원인 진단 - [WARN] AS-IS/TO-BE CSV 스키마 차이가 10개 있습니다. → 원인: AS-IS와 TO-BE가 같은 산출물 구조가 아니거나, TO-BE에 신규 검증/보안/학습 결과가 추가된 영향입니다. / 조치: 핵심 CSV 스키마만 표준화하고 부가 기능 CSV는 부록/TOBE_ONLY로 분리 - [WARN] 핵심 통계 지표 차이가 7개 있습니다. → 원인: 표본 수, 음성 변환/특징 추출 설정, 산식, 이상치 처리, STT/WPM 반영 차이에 의해 실제 수치가 달라졌을 수 있습니다. / 조치: TO-BE n≥900 체인이 완성된 뒤 다시 비교하고, 그때도 차이가 남으면 AS-IS/TO-BE 방법 차이 설명 문단으로 정리 ## 3. 메트릭 비교 현황 - MATCH: 15 - WARN: 4 - DIFF: 32 - ASIS_ONLY: 30 - TOBE_ONLY: 226 ## 4. CSV 스키마 비교 현황 - MATCH: 2 - ROW_DIFF: 0 - SCHEMA_DIFF: 10 - ASIS_ONLY: 13 - TOBE_ONLY: 113 ## 5. 권장 프로세스 1. AS-IS 경로를 `C:\jupyter_env\datasets0413`로 지정한다. 2. TO-BE는 현재 `C:\AI\sci_voc_bot` 결과 슬롯을 기준으로 비교한다. 3. 파일 인벤토리 → CSV 스키마 → 핵심 메트릭 → 문서/리포트 산출물 순서로 확인한다. 4. TO-BE의 건수 차이는 원인 후보로만 보고, 최종 판정은 stress_score, 상관 방향, 효과크기 등 결과값 유사도 중심으로 확인한다. 5. 차이가 설명 가능하면 논문에는 '재현 분석에서 주요 방향성 유지'로 쓰고, 설명 불가능하면 C 최종 분석을 다시 실행한다. ## 6. 생성 파일 - `results/tables/asis_tobe/file_inventory_compare.csv` - `results/tables/asis_tobe/csv_schema_compare.csv` - `results/tables/asis_tobe/key_metric_compare.csv` - `results/tables/asis_tobe/difference_root_cause_analysis.csv` - `results/tables/asis_tobe/difference_action_plan.csv` - `results/tables/asis_tobe/difference_problem_trace.csv` - `reports/asis_tobe/asis_tobe_comparison_report_ko.docx` - `reports/asis_tobe/asis_tobe_comparison_report_en.docx`
산출물 텍스트: results/manuscript_text/future_analysis/additional_research_manuscript_ko.md
# E. 추가연구 계획 및 한계 보완 방향 본 연구의 한계로는 첫째, 외부 준거 라벨이 없어 예측 타당도를 직접 검증하지 못했다는 점이 있다. 둘째, 단일 콜센터 데이터를 사용하였기 때문에 다른 센터와 업무 유형으로의 일반화에는 제약이 있다. 셋째, 개인 간 기저 음성 특징의 차이를 통제하지 않았으므로 화자별 음성 습관이나 성별·연령에 따른 차이가 stress_score에 혼입될 가능성이 있다. 넷째, WPM은 Whisper STT 결과를 기반으로 산출되었으므로 전사 오류가 발화속도 계산에 영향을 줄 수 있다. 이러한 한계를 보완하기 위해 후속연구에서는 다섯 가지 확장 분석이 필요하다. 첫째, 전문가 평정, 상담 품질 등급, CSAT, 민원 또는 재통화 여부와 같은 외부 준거 라벨을 수집하여 stress_score의 준거 타당도와 예측 타당도를 검증해야 한다. 둘째, 복수 콜센터와 복수 업무 유형 데이터를 추가하여 센터 간 재현성과 외적 타당도를 확인해야 한다. 셋째, speaker_id, gender, speaker_role 등 화자 메타데이터를 확보하여 화자별 z-score, 성별 보정, 혼합효과모형 등 화자 정규화 방법을 적용해야 한다. 넷째, KsponSpeech와 같은 정답 전사문 기반 데이터셋과 VOC 일부 수동 전사 Goldset을 활용하여 WER, CER, WPM 오차율 및 STT 오류가 stress_score에 미치는 영향을 추가 검증해야 한다. 다섯째, 음성 임베딩, 텍스트 감성점수, 화자분리 및 상담 메타데이터를 결합한 다채널 감성 분석으로 현재의 해석 가능한 선형 지수를 고도화할 필요가 있다. 현재 E 단계 산출물은 후속연구 설계와 실행 템플릿이다. 따라서 실제 외부 준거 라벨, 다센터 데이터, 화자 ID가 확보되기 전까지는 본 연구의 결과로 해석하지 않고, 논문의 한계 및 후속연구 절에 제한적으로 반영한다. 현재 stress_score 유효 행 수: 1000
결과 해석
연구 결과를 축별로 분리하면 본문 타당도 결과를 유지하면서도 화자분리 성과를 부록과 후속 연구로 활용할 수 있다.
한계 및 논문 반영 기준
TO-BE와 AS-IS의 수치 차이를 성능 향상 또는 악화로 단정하지 않는다.
6. 교수님 보고 및 발표 준비
논문 작성용 상세 본문
발표에서는 완료된 기술 결과와 아직 검증되지 않은 해석을 명확히 분리해야 한다.
현재 안전하게 보고할 수 있는 내용은 1,000통화 실제 화자분리, 57,619 세그먼트, 2,000 통화-화자 슬롯, 28,149 응답쌍, 세그먼트 acoustic feature 입력 생성 완료이다.
고객/상담사 역할 확정, 고객-only stress, 상담사 영향, S0 stress→S1 stress 인과, high-stress segment 확정은 아직 사용할 수 없다.
연구 연속성은 0413 본문을 유지하고 TO-BE와 화자분리를 부록·재현성·후속 분석으로 연결하는 방식으로 설명한다.
발표와 논문에서는 완료된 기술 결과, 탐색적 해석, 미완료 항목을 서로 다른 표현으로 제시해야 한다.
실제 화자분리, 세그먼트 수, 슬롯 수, 응답쌍, acoustic feature 입력 생성은 완료 사실이다. 고객·상담사 역할 확정과 segment stress 기반 영향 분석은 아직 완료되지 않았다.
따라서 발표 문구는 '시간 순서 기반 연관성 후보'와 '후속 검증 필요'를 사용하고 인과 표현을 피한다.
분석 수치 및 결과표
표 6-1. 사용 가능한 표현
| 표현 | 사용 |
|---|---|
| 실제 화자분리 1,000건 완료 | 가능 |
| SPEAKER 세그먼트 57,619건 생성 | 가능 |
| 통화-화자 슬롯 2,000건 구성 | 가능 |
| 응답쌍 기반 시간 순서 후보 분석 | 가능 |
| segment acoustic feature 입력 생성 | 가능 |
표 6-2. 금지 또는 보류 표현
| 표현 | 판정 | 이유 |
|---|---|---|
| 고객/상담사 역할 확정 | 금지 | role mapping 미검증 |
| 고객-only stress 완료 | 금지 | segment stress 미완료 |
| 상담사가 고객 스트레스에 영향 | 금지 | 인과 근거 없음 |
| 고스트레스 세그먼트 확정 | PENDING | stress_score 0건 |
표 6-3. 연구 연속성
| 기준 | 현재 위치 | 논문 처리 |
|---|---|---|
| 0413 n=998 | 본문 핵심 | 유지 |
| TO-BE n=1,000 | 재현성 | 부록 |
| 화자분리 7~11번 | 확장 분석 | 부록/후속 연구 |
| 12번 이후 | segment stress | 완료 후 재검토 |
표 6-Q. 연구 질문
| 번호 | 연구 질문 |
|---|---|
| 1 | 교수님 보고에서 완료 사실과 PENDING을 어떻게 구분할 것인가? |
| 2 | 논문 본문·부록·후속 연구의 경계를 어떻게 설명할 것인가? |
표 6-M. 분석 방법 및 도구
| 순서 | 방법/도구 |
|---|---|
| 1 | Claim guard |
| 2 | Professor Q&A |
| 3 | Presentation evidence map |
| 4 | Submission readiness |
분석 그림 및 도식
원본 분석 산출물 연계
산출물 JSON: results/runtime/scie_submission_readiness_status.json
전체 행 포함
| 경로 | 값 |
|---|---|
| profile | n1000 |
| snapshot_path | C:\AI\sci_voc_bot\results_by_count\n1000\snapshot |
| snapshot_exists | True |
| stt_path | C:\AI\sci_voc_bot\data\voc\stt\voc_whisper_small_results.csv |
| stt_rows | 1000 |
| stt_success_unique | 992 |
| stt_failed_or_empty | 8 |
| stt_model_detected | small |
| feature_path | C:\AI\sci_voc_bot\data\voc\features\voc_features.csv |
| feature_n | 442 |
| stress_path | |
| stress_n | 0 |
| wpm_mean | 83.4118 |
| stress_mean | |
| generated_at | 2026-07-03 13:20:41 |
| analysis_policy | ASIS_MAIN |
| status | READY_WITH_REVIEW |
| main_recommendation | 3번 논문 안전안을 본문 기준으로 유지하고, 2번 TO-BE 재산출안은 재현성 검증으로 분리 |
산출물 CSV: results/tables/meeting_0627/additional_research_why_method_direction.csv (행 5, 열 5)
전체 행 포함
| 주제 | 왜 필요한가 | 왜 이 기법인가 | 방향성 | 이번 보고에서의 표현 |
|---|---|---|---|---|
| 외부 준거 라벨 확보 | 현재 연구는 외부 정답 라벨 없이 음성 특징과 stress_score의 내적 일관성을 검토한다. 심사자는 '이 점수가 실제 스트레스나 민원 위험과 연결되는가'를 물을 가능성이 높다. | 전문가 평정, QA 점수, CSAT, 민원/재통화 여부를 준거로 두면 stress_score와 실제 운영 결과 간 관련성을 직접 검증할 수 있다. | 200~500콜 Goldset을 만들고 stress_score와 전문가 평정/민원위험의 상관, ANOVA, AUROC를 분석한다. | 현재 논문 결과가 아니라 후속연구 계획으로 제시한다. 현재 원고에서는 '준거타당도 검증 필요'로 제한한다. |
| 다센터/다업무 일반화 검증 | 단일 콜센터 VOC만으로는 결과가 특정 업무, 특정 기간, 특정 녹취 환경에 의존했을 수 있다. | 센터·업무유형·기간별로 같은 지표를 다시 산출하면 지표의 외적 타당도와 재현성을 확인할 수 있다. | 센터 ID, 업무유형, 수집기간을 추가하여 층화 상관분석, 그룹별 ANOVA, 안정성 요약을 수행한다. | 일반화 한계를 인정하고, 다센터 검증을 차기 데이터 확장 방향으로 제시한다. |
| 화자 정규화 | pitch, energy, wpm은 개인의 기저 음성 차이에 영향을 받는다. 화자 차이를 통제하지 않으면 스트레스 효과와 개인차가 섞일 수 있다. | 화자별 z-score, 성별/역할 보정, mixed-effects model은 개인 기준선과 상황 변화 효과를 분리하는 데 적합하다. | speaker_id, 성별, 고객/상담사 역할, 반복통화 정보를 확보하여 화자 기준선 보정 전후 결과를 비교한다. | 현재는 전역 z-score 기반 지수이며, 화자 정규화는 후속 보완 분석으로 제시한다. |
| STT/WPM 신뢰도 검증 | WPM은 STT 전사문에서 계산되므로 전사 오류가 있으면 지수 일부가 왜곡될 수 있다. | KsponSpeech처럼 정답 전사문이 있는 기준셋을 사용하면 Whisper 결과와 정답을 비교해 WER/CER를 산출할 수 있다. | KsponSpeech n=300~500 또는 VOC Goldset 정답전사로 WER/CER와 WPM 민감도 분석을 수행한다. | STT 검증은 본 연구의 기술적 신뢰도 보조 근거이며, WPM은 보조 변수로 제한 해석한다. |
| 음성 임베딩/다채널 감성 확장 | 현재 지수는 해석 가능한 4개 특징 중심이므로 복잡한 감정·스트레스 패턴을 모두 포착하지 못할 수 있다. | wav2vec/HuBERT 계열 임베딩과 텍스트 감성·키워드 흐름을 결합하면 비선형 음성 패턴과 VOC 맥락을 함께 반영할 수 있다. | 현재 지수는 설명 가능한 기준모형으로 유지하고, 임베딩 기반 모형은 후속 비교모형으로 설계한다. | 현재 논문에서는 과도한 모델 확장보다 지수의 구성타당도와 한계를 명확히 한다. |
산출물 CSV: results/tables/meeting_0627/meeting_0627_current_status.csv (행 1, 열 17)
전체 행 포함
| profile | snapshot_found | voc_stt_n | features_n | stress_n | kspon_stt_n | wpm_nonzero | wpm_mean | quality_fail | quality_warn | corr_energy | corr_pitch | corr_wpm | corr_duration | wer | cer | generated_at |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| n1000 | False | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2026-06-26 23:34:06 |
산출물 CSV: results/tables/meeting_0627/professor_questions_0627.csv (행 5, 열 2)
전체 행 포함
| 교수님 확인 질문 | 왜 필요한가 |
|---|---|
| 현재 논문은 '내적 구성타당도 검증' 중심으로 제출하고, 외부 준거 라벨 검증은 후속연구로 분리하는 방향이 적절한가? | 현재 데이터 구조에서 과장 주장을 피하기 위한 연구 범위 확정이 필요하다. |
| 22번 추가연구 항목 중 이번 원고에 반드시 포함할 한계 항목과 후속연구 항목의 우선순위는 무엇인가? | 원고 분량과 심사 리스크를 고려해 핵심 한계만 선별해야 한다. |
| WPM/STT 신뢰도는 현재 원고에서 어느 수준까지 설명하는 것이 적절한가? | STT 결과가 불안정하면 WPM을 핵심 변수로 강하게 해석하면 위험하다. |
| 향후 Goldset 라벨링은 전문가 평정 3점 척도, 민원위험 등급, QA 점수 중 무엇을 우선 준거로 둘지 확인이 필요하다. | 후속연구의 준거타당도 설계 방향을 정해야 한다. |
| SCI급 학회지 투고와 국내 학회지 투고 중 현재 데이터 완성도 기준으로 우선순위를 어떻게 잡을지 확인이 필요하다. | 투고처에 따라 원고의 주장 강도와 보완 분석 수준이 달라진다. |
산출물 CSV: results/tables/meeting_0627/why_why_why_structure.csv (행 6, 열 2)
전체 행 포함
| 질문 | 답변 |
|---|---|
| 왜 추가연구를 해야 하는가? | 현재 연구는 VOC 실데이터 기반이라는 강점이 있지만 외부 준거 라벨, 다센터 검증, 화자 정규화, STT/WPM 신뢰도 보완이 남아 있다. 이 한계를 숨기지 않고 후속연구 설계로 제시해야 논문의 방어력이 높아진다. |
| 왜 지금 추가연구를 논의해야 하는가? | 학회지 제출 전 교수님께 연구의 한계와 보완 방향을 명확히 보고해야 한다. 그래야 현재 논문에서 주장할 수 있는 범위와 후속연구로 넘겨야 할 범위를 구분할 수 있다. |
| 왜 22번 추가연구를 현재 결과처럼 쓰면 안 되는가? | 외부 라벨, 다센터 데이터, speaker_id 같은 입력이 아직 없으면 실제 검증 결과가 아니라 계획과 템플릿이다. 현재 결과로 과장하면 심사에서 취약해진다. |
| 왜 구성타당도 중심인가? | 현재 데이터에는 스트레스 정답 라벨이 없다. 따라서 지도학습 예측 성능이 아니라, 이론적으로 관련된 음향변수와 stress_score의 관계를 검토하는 구성타당도 접근이 가장 안전하다. |
| 왜 STT 검증을 별도로 두는가? | VOC 데이터에는 정답 전사문이 없기 때문에 STT 정확도를 직접 평가하기 어렵다. 정답이 있는 KsponSpeech로 Whisper의 WER/CER를 검증한 뒤 VOC 분석의 기술적 한계를 설명해야 한다. |
| 왜 Goldset이 필요한가? | 외부 준거 라벨이 있어야 stress_score가 실제 민원위험, 상담품질, 전문가 스트레스 평정과 연결되는지 검증할 수 있다. 이는 후속연구에서 준거타당도를 확보하는 핵심이다. |
산출물 CSV: results/tables/scie_submission/scie_readiness_gates.csv (행 7, 열 5)
전체 행 포함
| gate | requirement | current_status | decision | action |
|---|---|---|---|---|
| 최종 분석본 고정 | 본문 주 분석 기준을 AS-IS 또는 TO-BE 중 하나로 고정 | ASIS_MAIN | PASS | 3번 논문 안전안이면 AS-IS 0413을 본문 기준으로 유지하고, 2번은 부록/재현성 검증으로 분리 |
| TO-BE STT 성공 수 | STT 성공 unique n ≥ 900 | 992 | PASS | n이 부족하면 05 STT 이어실행 후 실패/빈 row 제거 |
| WPM 반영 | WPM 평균 > 0 | 83.4118 | PASS | 07 Feature와 10 Stress Index를 STT 성공 결과 기준으로 재산출 |
| Feature/Stress 표본 수 | Feature n ≥ 900 및 Stress n ≥ 900 | feature=442, stress=0 | BLOCK | TO-BE 재산출 branch 02 실행 또는 snapshot 갱신 |
| KsponSpeech STT 검증 | WER/CER 표, 평균, 표준편차, 95% CI 필요 | 별도 확인 필요 | REVIEW | KsponSpeech 100~500개 샘플에서 small/medium WER·CER 비교표 생성 |
| 재현성 패키지 | 환경표, 실행 로그, 산식표, 제외 기준표 필요 | v104 패키지 생성됨 | REVIEW | 생성된 scie_reproducibility_checklist.csv를 확인하고 빈 항목 보완 |
| 통계 보강 | 효과크기, 95% CI, 민감도, VIF, 한계 명시 | 별도 확인 필요 | REVIEW | construct validity, bootstrap, sensitivity, OLS/VIF 결과표를 최종 원고 표와 연결 |
산출물 CSV: results/tables/submission_readiness_check.csv (행 19, 열 4)
전체 행 포함
| check | value | status | required_fix |
|---|---|---|---|
| file_exists:descriptive_statistics | C:\AI\sci_voc_bot\results\tables\descriptive_statistics.csv | pass | |
| file_exists:stress_index_feature_quality | C:\AI\sci_voc_bot\results\tables\stress_index_feature_quality.csv | pass | |
| file_exists:correlation_validity | C:\AI\sci_voc_bot\results\tables\correlation_validity.csv | pass | |
| file_exists:bootstrap_ci | C:\AI\sci_voc_bot\results\tables\bootstrap_ci.csv | pass | |
| file_exists:weight_sensitivity | C:\AI\sci_voc_bot\results\tables\weight_sensitivity.csv | pass | |
| file_exists:loo_stability | C:\AI\sci_voc_bot\results\tables\loo_stability.csv | pass | |
| file_exists:anova_profile | C:\AI\sci_voc_bot\results\tables\anova_profile.csv | pass | |
| file_exists:construct_validity_summary | C:\AI\sci_voc_bot\results\tables\construct_validity_summary.csv | pass | |
| file_exists:stt_reliability | C:\AI\sci_voc_bot\results\tables\stt_reliability.csv | pass | |
| VOC sample size for final paper | 100 | warning | 최종 제출본은 n=998 또는 최소 900건 이상 권장. 현재 테스트 결과라면 전체/1000으로 재실행 |
| WPM normality / STT included | status=constant_or_missing_set_to_zero, unique_n=1, mean=0.0 | fail | --skip-stt 없이 Whisper STT 포함 실행. wpm unique_n > 1 확인 필요 |
| KsponSpeech STT reliability sample size | 100 | warning | SCI 본문용은 KsponSpeech n=300~500 권장. 100개는 예비 테스트로 표기 |
| construct_validity:convergent validity - energy_mean | 0.6043203394066413 | pass | |
| construct_validity:convergent validity - pitch_mean | 0.6585061599866662 | pass | |
| construct_validity:bootstrap CI lower - energy_mean | 0.4637559985716991 | warning | n=998 전체 분석으로 재실행하거나 Discussion에서 제한적으로 해석 |
| construct_validity:bootstrap CI lower - pitch_mean | 0.5000862582707163 | pass | |
| construct_validity:minimum weight sensitivity r | 0.8359470583389365 | warning | n=998 전체 분석으로 재실행하거나 Discussion에서 제한적으로 해석 |
| construct_validity:ANOVA eta_squared - energy_mean | 0.293516494516232 | pass | |
| construct_validity:ANOVA eta_squared - pitch_mean | 0.4044140844049427 | pass |
산출물 CSV: results/tables/submission_readiness_checklist.csv (행 15, 열 5)
전체 행 포함
| item | status | severity | message | recommended_action |
|---|---|---|---|---|
| Final sample size | FAIL | error | 현재 분석 표본 n=100입니다. 파일럿/테스트 결과입니다. | n=100 파일럿 문장을 삭제하고 전체/998건 기준으로 재분석 후 원고를 생성하세요. |
| WPM measurement | FAIL | error | WPM이 상수/결측 처리되어 현재 지수에 정상 반영되지 않았습니다. | VOC Whisper STT 포함으로 재실행하고, WPM은 최종 원고에서 보조 변수로 제한 해석하세요. |
| Duration direction in formula | PASS | info | stress_raw 공식에서 duration_sec가 음(-) 방향으로 반영됩니다. | 원고 전 구간에서 z_energy + z_pitch + z_wpm - z_duration 구조로 통일하세요. |
| Duration direction in manuscript text | PASS | info | duration 방향성 충돌 표현이 감지되지 않았습니다. | 계속 유지하세요. |
| STT reliability claim level | WARN | warning | WER=1.000, CER=1.000입니다. '신뢰도 확보'라고 강하게 주장하기 어렵습니다. | STT 결과는 WPM의 측정 한계와 보조 변수 해석 근거로만 사용하세요. |
| MDPI metadata: title_en | PASS | info | title_en 항목이 존재합니다. | 제출 전 최종 확인하세요. |
| MDPI metadata: authors | PASS | info | authors 항목이 존재합니다. | 제출 전 최종 확인하세요. |
| MDPI metadata: keywords | PASS | info | keywords 항목이 존재합니다. | 제출 전 최종 확인하세요. |
| MDPI metadata: funding | WARN | warning | funding에 확인/수정이 필요한 placeholder가 남아 있습니다. | 제출 전 실제 기관/교수님 확인 내용으로 바꾸세요. |
| MDPI metadata: irb_statement | WARN | warning | irb_statement에 확인/수정이 필요한 placeholder가 남아 있습니다. | 제출 전 실제 기관/교수님 확인 내용으로 바꾸세요. |
| MDPI metadata: informed_consent_statement | WARN | warning | informed_consent_statement에 확인/수정이 필요한 placeholder가 남아 있습니다. | 제출 전 실제 기관/교수님 확인 내용으로 바꾸세요. |
| MDPI metadata: data_availability_statement | PASS | info | data_availability_statement 항목이 존재합니다. | 제출 전 최종 확인하세요. |
| MDPI metadata: conflicts_of_interest | PASS | info | conflicts_of_interest 항목이 존재합니다. | 제출 전 최종 확인하세요. |
| MDPI metadata: author_contributions | PASS | info | author_contributions 항목이 존재합니다. | 제출 전 최종 확인하세요. |
| Generated manuscript text cleanup | FAIL | error | 생성 원고에 제출 전 제거해야 할 표현이 있습니다: 파일럿 n=100 표현; pilot 표현; 자동생성 목차 marker; 자동생성 그림 marker; 그림 X placeholder; 임상 진단 과장 표현; 검증된 탐지모형 과장 표현 | 목차별 원고를 재작성하거나 v24 엄격 제출 모드로 DOCX를 다시 생성하세요. |
산출물 텍스트: results/manuscript_text/meeting_0627/meeting_0627_professor_report_n1000_ko.md
# 6월 27일 논문모임 교수님 보고자료 ## 보고 핵심 - 22번 추가연구(E)는 현재 논문 결과를 과장하기 위한 기능이 아니라, 현재 연구 한계를 방어 가능한 후속연구 설계로 전환하기 위한 기능입니다. - 현재 논문은 외부 정답 라벨이 없는 조건에서 stress_score의 내적 구성타당도, 강건성, 민감도 중심으로 정리하는 것이 안전합니다. - 외부 준거 라벨, 다센터 검증, 화자 정규화, STT/WPM 신뢰도 검증은 후속연구로 분리해야 합니다. ## 현재 상태 - profile: n1000 - voc_stt_n: 0 - features_n: 0 - stress_n: 0 - kspon_stt_n: 0 - wpm_nonzero: 0 - quality_fail: 0 - quality_warn: 0 ## 왜? 왜? 왜? ### 왜 추가연구를 해야 하는가? 현재 연구는 VOC 실데이터 기반이라는 강점이 있지만 외부 준거 라벨, 다센터 검증, 화자 정규화, STT/WPM 신뢰도 보완이 남아 있다. 이 한계를 숨기지 않고 후속연구 설계로 제시해야 논문의 방어력이 높아진다. ### 왜 지금 추가연구를 논의해야 하는가? 학회지 제출 전 교수님께 연구의 한계와 보완 방향을 명확히 보고해야 한다. 그래야 현재 논문에서 주장할 수 있는 범위와 후속연구로 넘겨야 할 범위를 구분할 수 있다. ### 왜 22번 추가연구를 현재 결과처럼 쓰면 안 되는가? 외부 라벨, 다센터 데이터, speaker_id 같은 입력이 아직 없으면 실제 검증 결과가 아니라 계획과 템플릿이다. 현재 결과로 과장하면 심사에서 취약해진다. ### 왜 구성타당도 중심인가? 현재 데이터에는 스트레스 정답 라벨이 없다. 따라서 지도학습 예측 성능이 아니라, 이론적으로 관련된 음향변수와 stress_score의 관계를 검토하는 구성타당도 접근이 가장 안전하다. ### 왜 STT 검증을 별도로 두는가? VOC 데이터에는 정답 전사문이 없기 때문에 STT 정확도를 직접 평가하기 어렵다. 정답이 있는 KsponSpeech로 Whisper의 WER/CER를 검증한 뒤 VOC 분석의 기술적 한계를 설명해야 한다. ### 왜 Goldset이 필요한가? 외부 준거 라벨이 있어야 stress_score가 실제 민원위험, 상담품질, 전문가 스트레스 평정과 연결되는지 검증할 수 있다. 이는 후속연구에서 준거타당도를 확보하는 핵심이다. ## 교수님께 확인할 질문 - 현재 논문은 '내적 구성타당도 검증' 중심으로 제출하고, 외부 준거 라벨 검증은 후속연구로 분리하는 방향이 적절한가? - 이유: 현재 데이터 구조에서 과장 주장을 피하기 위한 연구 범위 확정이 필요하다. - 22번 추가연구 항목 중 이번 원고에 반드시 포함할 한계 항목과 후속연구 항목의 우선순위는 무엇인가? - 이유: 원고 분량과 심사 리스크를 고려해 핵심 한계만 선별해야 한다. - WPM/STT 신뢰도는 현재 원고에서 어느 수준까지 설명하는 것이 적절한가? - 이유: STT 결과가 불안정하면 WPM을 핵심 변수로 강하게 해석하면 위험하다. - 향후 Goldset 라벨링은 전문가 평정 3점 척도, 민원위험 등급, QA 점수 중 무엇을 우선 준거로 둘지 확인이 필요하다. - 이유: 후속연구의 준거타당도 설계 방향을 정해야 한다. - SCI급 학회지 투고와 국내 학회지 투고 중 현재 데이터 완성도 기준으로 우선순위를 어떻게 잡을지 확인이 필요하다. - 이유: 투고처에 따라 원고의 주장 강도와 보완 분석 수준이 달라진다.
산출물 텍스트: results/manuscript_text/scie_submission/english_manuscript_outline.md
# SCIE English Manuscript Outline ## Working Title A Reproducible Framework for Unsupervised Voice-of-Customer Stress Index Estimation Using Speech-Derived Acoustic and Textual Features ## Core Claim This study proposes and validates a non-clinical, unsupervised stress-related index for Korean call-center voice-of-customer audio by integrating acoustic features and STT-derived speaking-rate measures. ## Recommended Structure 1. Introduction 2. Related Work 3. Data and Preprocessing 4. STT Validation using KsponSpeech 5. Stress Index Construction 6. Statistical Validation 7. Results 8. AS-IS/TO-BE Reproducibility Analysis 9. Discussion 10. Limitations 11. Conclusion ## Safe Positioning - Use "stress-related index" rather than "clinical stress diagnosis". - Use "construct-validity evidence" rather than "fully validated model". - Keep AS-IS and TO-BE results separated. One is the main analysis, the other is a reproducibility or robustness analysis. ## Required Tables - Table 1. Dataset and preprocessing summary - Table 2. STT validation on KsponSpeech: WER/CER - Table 3. Feature definitions and stress-score formula - Table 4. Descriptive statistics - Table 5. Correlation and construct-validity evidence - Table 6. ANOVA/effect-size results - Table 7. Sensitivity/robustness results - Table 8. AS-IS/TO-BE reproducibility comparison ## Required Figures - Figure 1. End-to-end pipeline - Figure 2. Stress-index distribution - Figure 3. Feature correlation heatmap - Figure 4. AS-IS/TO-BE workflow separation
산출물 텍스트: results/manuscript_text/scie_submission/scie_action_plan_ko.md
# SCIE 투고 준비 패키지 - 생성시각: 2026-07-03 13:20:41 - 기준 profile: `n1000` - 분석 정책: `ASIS_MAIN` - STT 성공 unique: **992** - Feature n: **442** - Stress n: **0** - WPM 평균: **83.4118** ## 핵심 판단 1. 본문에는 하나의 최종 분석본만 사용해야 합니다. 2. 현재 기본 권장안은 **3번 논문 안전안 본문 유지 + 2번 TO-BE 재산출안 재현성 검증 분리**입니다. 3. KsponSpeech WER/CER 검증표가 SCIE 투고 전 필수 보강 항목입니다. 4. 재현성 패키지에는 환경표, 제외 기준표, 산식표, snapshot 경로, 실행 로그가 들어가야 합니다. ## 바로 할 일 1. `kspon_validation_plan.csv` 기준으로 STT WER/CER 검증표 생성 2. `final_dataset_decision_matrix.csv`에서 본문 기준을 교수님과 확정 3. `scie_reproducibility_checklist.csv`의 NEEDED 항목 보완 4. `statistical_validation_checklist.csv` 기준으로 효과크기·CI·민감도·VIF 표 확인 5. `journal_target_strategy.csv`에서 1차 후보 3개 선정 ## 원고 안전 문장 > This study proposes and evaluates a non-clinical, stress-related index for Korean call-center VOC audio by integrating acoustic features and STT-derived speaking-rate measures. The AS-IS analysis is treated as the main analysis, while the TO-BE automated pipeline is reported as a reproducibility check.
결과 해석
연구 방어의 핵심은 기술적으로 완료된 결과를 충분히 보여주되, 역할과 stress 해석의 미완료 상태를 숨기지 않는 것이다.
한계 및 논문 반영 기준
통계적 연관성과 인과 관계를 구분해야 한다.
7. 실제 화자분리 상세보고서
논문 작성용 상세 본문
pyannote 기반 화자분리를 TO-BE 1,000개 통화에 적용해 SPEAKER 세그먼트 57,619건을 생성했다.
각 세그먼트는 call_id, speaker_label, start_sec, end_sec, duration_sec 등의 키를 가지며, 후속 feature/STT/stress 병합의 기준 행으로 사용된다.
화자분리 성공은 음성 구간과 화자 후보가 분리됐다는 의미이며, 고객과 상담사의 실제 역할이 확정됐다는 의미는 아니다.
화자분리는 통화 전체 음성을 시간 구간과 화자 후보 라벨로 분할하는 단계이다. 결과는 57,619개 세그먼트이며 각 행은 통화, 화자 후보, 시작·종료 시각, 구간 길이 정보를 가진다.
세그먼트는 후속 acoustic feature, STT, WPM, stress_score를 연결하는 최소 분석 단위이다.
SPEAKER_00과 SPEAKER_01은 상대적 클러스터 라벨이므로 실제 고객 또는 상담사 역할로 확정해서는 안 된다.
분석 수치 및 결과표
표 7-1. 화자분리 결과 요약
| 항목 | 값 | 상태 |
|---|---|---|
| 입력 통화 | 1,000 | 완료 |
| SPEAKER 세그먼트 | 57,619 | 완료 |
| 통화-화자 슬롯 | 2,000 | 완료 |
| 상태 코드 | DONE_SPEAKER_DIARIZATION | 완료 |
표 7-2. 세그먼트 진단 필드
| 필드 | 설명 |
|---|---|
| file | 원본 통화 파일 |
| duration_sec | 통화 길이 |
| sample_rate | 샘플레이트 |
| channels | 채널 수 |
| speaker_count | 검출 화자 수 |
| segment_count | 세그먼트 수 |
| status/error | 실제 오류 여부 |
표 7-Q. 연구 질문
| 번호 | 연구 질문 |
|---|---|
| 1 | 1,000개 통화에서 실제 화자 세그먼트를 안정적으로 생성했는가? |
| 2 | 화자분리 결과가 후속 병합의 기준 키로 활용 가능한가? |
표 7-M. 분석 방법 및 도구
| 순서 | 방법/도구 |
|---|---|
| 1 | pyannote diarization |
| 2 | Two-speaker constraint |
| 3 | Segment diagnostics |
| 4 | Truth-gate verification |
분석 그림 및 도식
원본 분석 산출물 연계
산출물 CSV: research_continuity/17_speaker_diarization_v171/speaker_diarization_segments_v171.csv (행 0, 열 1)
전체 행 포함
| message |
|---|
산출물 CSV: research_continuity/20_speaker_oneclick_addon_v173/speaker_diarization_segments_v173_from_v171.csv (행 0, 열 1)
전체 행 포함
| message |
|---|
산출물 CSV: research_continuity/24_speaker_actual_diarization_v176/speaker_acoustic_feature_candidates_v176.csv (행 0, 열 2)
전체 행 포함
| item | value |
|---|
산출물 CSV: research_continuity/24_speaker_actual_diarization_v176/speaker_actual_segments_v176.csv (행 0, 열 9)
전체 행 포함
| file_id | audio_path | segment_index | speaker_label | start_sec | end_sec | duration_sec | source | actual_diarization |
|---|
산출물 CSV: research_continuity/24_speaker_actual_diarization_v176/speaker_interaction_categories_v176.csv (행 7, 열 5)
전체 행 포함
| category_no | category | state | analysis_view | paper_policy |
|---|---|---|---|---|
| 7-1 | AI-IS/0413 기준 불러오기 | DONE | 기존 stress 산식·0413 본문 기준은 유지 | 본문 유지 |
| 7-2 | TO-BE 음성 입력 확인 | NEEDS_AUDIO_INPUT | 입력 음성 0건 | 확장분석 입력 |
| 7-3 | 실제 화자분리 실행 | READY_TO_RUN_ACTUAL_DIARIZATION_NOT_EXECUTED | 세그먼트 0건 / pyannote=True / token=False | 가짜 분리 금지 |
| 7-4 | 화자별 음성 파일 분리 | WAITING_FOR_ACTUAL_DIARIZATION | SPEAKER_00/SPEAKER_01 등 화자별 wav를 로컬 생성 | 원음성은 공개 배포 제외 |
| 7-5 | 고객/상담원 역할 매핑 | NEEDS_ROLE_MAPPING | speaker_role_mapping_template_v176.csv에서 customer/agent 수동 확인 | 역할 검증 전 고객-only 주장 금지 |
| 7-6 | 고객 고스트레스 이후 상담원 변화 | WAITING_FOR_ROLE_MAPPING_AND_SPEAKER_STT | agent_reactivity_delta 등 후보 지표 | 부록·후속연구 후보 |
| 7-7 | Word 통합 보고서 | DONE | 6번 전체 내용 + 7번 실제 화자분리 상태 포함 | 교수님 검토용 |
산출물 CSV: research_continuity/24_speaker_actual_diarization_v176/speaker_interaction_metrics_v176.csv (행 0, 열 2)
전체 행 포함
| item | value |
|---|
산출물 CSV: research_continuity/24_speaker_actual_diarization_v176/speaker_interaction_readiness_v176.csv (행 0, 열 2)
전체 행 포함
| item | value |
|---|
산출물 CSV: research_continuity/24_speaker_actual_diarization_v176/speaker_role_mapping_template_v176.csv (행 1, 열 6)
전체 행 포함
| file_id | speaker_label | role | mapping_confidence | evidence | memo |
|---|---|---|---|---|---|
| 실제 화자분리 후 자동 생성 |
산출물 CSV: research_continuity/24_speaker_actual_diarization_v176/speaker_split_audio_manifest_v176.csv (행 0, 열 6)
전체 행 포함
| file_id | speaker_label | split_audio_path | segment_count | state | public_deploy |
|---|
산출물 CSV: research_continuity/24_speaker_actual_diarization_v176/speaker_tobe_audio_discovery_v179.csv (행 44, 열 5)
전체 행 포함
| priority | candidate_path | exists | audio_count_found | policy |
|---|---|---|---|---|
| 1 | C:\AI\sci_voc_bot\data\voc\audio_wav_16k | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 2 | C:\AI\sci_voc_bot\data\voc\audio_raw | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 3 | C:\AI\sci_voc_bot\data\tobe\audio_wav_16k | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 4 | C:\AI\sci_voc_bot\data\tobe\audio_raw | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 5 | C:\AI\sci_voc_bot\datasets0406\02_wav16k | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 6 | C:\AI\sci_voc_bot\datasets0413\02_wav16k | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 7 | C:\AI\sci_voc_bot\datasets0223\02_wav16k | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 8 | C:\AI\sci_voc_bot\VOC_full | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 9 | C:\AI\sci_voc_bot\VOC_sample1000_2 | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 10 | C:\AI\sci_voc_bot\resources\tobe\audio | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 11 | C:\AI\sci_voc_bot\resources\tobe\wav | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 12 | C:\AI\sci_voc_bot\resources\tobe\mp3 | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 13 | C:\jupyter_env\VOC_full | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 14 | C:\jupyter_env\VOC_sample1000_2 | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 15 | C:\jupyter_env\datasets0406\02_wav16k | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 16 | C:\jupyter_env\datasets0413\02_wav16k | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 17 | C:\jupyter_env\datasets0223\02_wav16k | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 18 | /mnt/data/v180_src/data/voc/audio_wav_16k | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 19 | /mnt/data/v180_src/data/voc/audio_raw | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 20 | /mnt/data/v180_src/data/tobe/audio_wav_16k | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 21 | /mnt/data/v180_src/data/tobe/audio_raw | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 22 | /mnt/data/v180_src/datasets0406/02_wav16k | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 23 | /mnt/data/v180_src/datasets0413/02_wav16k | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 24 | /mnt/data/v180_src/datasets0223/02_wav16k | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 25 | /mnt/data/v180_src/VOC_full | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 26 | /mnt/data/v180_src/VOC_sample1000_2 | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 27 | /mnt/data/v180_src/resources/tobe/audio | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 28 | /mnt/data/v180_src/resources/tobe/wav | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 29 | /mnt/data/v180_src/resources/tobe/mp3 | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 30 | /mnt/data/v180_src/resources/voc_audio | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 31 | /mnt/data/v180_src/resources/diarization_input | True | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 32 | /mnt/data/v173_src/research_continuity/03_tobe_extension_research/tobe_extension_actual_manifest_v150.json | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 33 | /mnt/data/v173_src/resources/diarization_input | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 34 | resources/diarization_input에 오디오가 없어 상태카드만 생성했습니다. 배포 실패 조건은 아닙니다. | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 35 | resources/diarization_input | True | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 36 | /mnt/data/v173_src/results/tables/asis_tobe/strict_original_csv_schema_compare_v169.csv | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 37 | /mnt/data/v173_src/results/tables/asis_tobe/strict_original_key_metric_compare_v169.csv | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 38 | /mnt/data/v173_src/results/tables/asis_tobe/csv_schema_compare_resolved_v169.csv | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 39 | /mnt/data/v173_src/results/tables/asis_tobe/key_metric_compare_resolved_v169.csv | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 40 | SCHEMA_DIFF/ROW_DIFF/ASIS_ONLY/TOBE_ONLY가 교수님용 경고로 노출 | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 41 | /mnt/data/v173_src/results/tables/asis_tobe/kspon_consistency_metrics_v162.csv | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 42 | /mnt/data/v173_src/resources/tobe/asis_tobe_comparison_visual.png | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 43 | /mnt/data/v173_src/resources/tobe/to_be_actual_visual_summary.png | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
| 44 | /mnt/data/v173_src/results/tables/asis_tobe/kspon_consistency_metrics_v160.csv | False | 0 | TO-BE/VOC analyzed audio auto-scan candidate |
산출물 CSV: research_continuity/24_speaker_actual_diarization_v176/speaker_tobe_audio_files_v179.csv (행 0, 열 4)
전체 행 포함
| order | audio_path | file_id | suffix |
|---|
산출물 텍스트: research_continuity/24_speaker_actual_diarization_v176/speaker_actual_diarization_report_v176.md
# SCI-VOC 7번 실제 화자분리 기반 고객-상담원 상호작용 확장분석 v180 - 상태: READY_TO_RUN_ACTUAL_DIARIZATION_NOT_EXECUTED - 실제 화자분리 완료: False - 입력 음성: 0건 - 입력 기준: 기존 TO-BE/VOC 분석 음성 자동 탐색 - 실제 세그먼트: 0건 - 본문 기준: 0413 AS-IS 통화 전체 음성 유지 - 본문 대체: 금지 - 고객-only 주장: 실제 diarization + role mapping 검증 전 금지 ## 교수님 설명 문장 > 7번은 기존 AI-IS/0413 산출 기준을 유지한 채 기존 TO-BE/VOC 분석에 사용한 음성 경로에서 실제 화자분리를 수행하여 고객 발화와 상담원 발화의 상호작용 변화를 보는 별도 확장분석입니다. pyannote 기반 실제 세그먼트와 고객/상담원 role mapping이 확인되기 전에는 고객-only 또는 상담원 영향 결과로 주장하지 않겠습니다. ## 실행 기준 - pyannote.audio와 PYANNOTE_AUTH_TOKEN/HF_TOKEN이 있으면 실제 SPEAKER_00/SPEAKER_01 세그먼트를 생성합니다. - 토큰 또는 pyannote가 없으면 가짜 분리를 만들지 않고 준비 필요 상태로 표시합니다. - 분리된 화자별 wav는 로컬 분석용이며 공개 배포에는 포함하지 않습니다.
결과 해석
1,000통화 전체에서 세그먼트가 생성되었으므로 화자별 발화량·턴·응답 구조를 계산할 수 있는 기반이 마련됐다.
한계 및 논문 반영 기준
화자분리 라벨은 상대적 SPEAKER_00/01이며 실제 업무 역할은 별도 검증이 필요하다.
8. SPEAKER 후보 슬롯 및 응답쌍 상세보고서
논문 작성용 상세 본문
통화별로 SPEAKER_00 후보와 SPEAKER_01 후보 슬롯을 각각 하나씩 유지해 총 2,000개의 통화-화자 슬롯을 구성했다.
SPEAKER_01이 실제 검출되지 않은 일부 통화도 0초 슬롯으로 보존해 통화별 2개 슬롯 구조가 깨지지 않도록 했다.
S0→S1 응답쌍은 시간 순서상 S0 발화 뒤에 S1 발화가 이어지는 후보 쌍이며, 역할이나 인과를 의미하지 않는다.
1,000통화마다 두 개의 화자 후보 슬롯을 유지해 총 2,000개의 통화-화자 레코드를 구성하였다.
실제 두 번째 화자가 검출되지 않은 40개 통화는 누락 처리하지 않고 0초 슬롯으로 보존하여 표본 구조를 유지했다.
28,149개 응답쌍은 시간 순서상 앞선 발화와 뒤따른 발화를 연결한 탐색 단위이며 역할과 인과를 의미하지 않는다.
분석 수치 및 결과표
표 8-1. 화자 후보 슬롯
| 항목 | 값 | 해석 |
|---|---|---|
| S0 슬롯 | 1,000 | 통화별 1개 |
| S1 슬롯 | 1,000 | 통화별 1개 |
| 실제 검출 S0 | 1,000 | 검출 |
| 실제 검출 S1 | 960 | 검출 |
| 미검출 S1 | 40 | 0초 슬롯 보존 |
표 8-2. 응답쌍 구성
| 항목 | 값 | 주의 |
|---|---|---|
| S0→S1 응답쌍 | 28,149 | 시간 순서 후보 |
| 고객/상담사 확정 | 미완료 | role mapping 필요 |
| 인과 해석 | 금지 | 관찰적 순서만 존재 |
표 8-Q. 연구 질문
| 번호 | 연구 질문 |
|---|---|
| 1 | 통화별 2개 화자 후보 슬롯을 누락 없이 구성했는가? |
| 2 | 응답쌍은 어떤 기준으로 생성되고 어떤 해석 한계를 가지는가? |
표 8-M. 분석 방법 및 도구
| 순서 | 방법/도구 |
|---|---|
| 1 | Balanced speaker slots |
| 2 | Missing-speaker preservation |
| 3 | Temporal response-pair generation |
| 4 | Role-mapping guard |
분석 그림 및 도식
원본 분석 산출물 연계
산출물 CSV: research_continuity/25_customer_agent_interaction_v182/agent_candidate_segments_v182.csv (행 28,233, 열 7)
대용량 표 요약·앞/뒤 표본 포함, 원본은 데이터 ZIP 수록
| audio_name | speaker | role_candidate | start_sec | end_sec | duration_sec | audio_path_private |
|---|---|---|---|---|---|---|
| 00__350002022300000.wav | SPEAKER_01 | agent_candidate | 4.267 | 7.439 | 3.172 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_01 | agent_candidate | 7.76 | 10.679 | 2.919 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_01 | agent_candidate | 10.797 | 10.932 | 0.135 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_01 | agent_candidate | 12.164 | 19.471 | 7.307 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_01 | agent_candidate | 19.977 | 20.281 | 0.304 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_01 | agent_candidate | 21.817 | 28.735 | 6.919 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_01 | agent_candidate | 29.123 | 32.718 | 3.594 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_01 | agent_candidate | 38.354 | 38.405 | 0.051 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_01 | agent_candidate | 38.911 | 45.425 | 6.514 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_01 | agent_candidate | 45.627 | 48.04 | 2.413 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_01 | agent_candidate | 49.593 | 54.487 | 4.894 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_01 | agent_candidate | 54.959 | 56.714 | 1.755 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_01 | agent_candidate | 56.849 | 57.22 | 0.371 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_01 | agent_candidate | 58.925 | 63.886 | 4.961 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_01 | agent_candidate | 64.122 | 69.623 | 5.501 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_01 | agent_candidate | 70.18 | 73.893 | 3.713 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_01 | agent_candidate | 74.416 | 78.601 | 4.185 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_01 | agent_candidate | 82.297 | 82.668 | 0.371 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_01 | agent_candidate | 86.887 | 88.203 | 1.316 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_01 | agent_candidate | 89.148 | 89.705 | 0.557 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_01 | agent_candidate | 89.789 | 90.059 | 0.27 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_01 | agent_candidate | 92.573 | 98.362 | 5.788 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_01 | agent_candidate | 99.779 | 104.909 | 5.13 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_01 | agent_candidate | 105.247 | 106.175 | 0.928 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_01 | agent_candidate | 117.97 | 120.957 | 2.987 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| ... 중간 28,183행은 전체 데이터 부록 ZIP에 원본으로 포함 ... | ||||||
| 09__350002022300997.wav | SPEAKER_01 | agent_candidate | 61.76 | 64.358 | 2.599 | C:\jupyter_env\VOC_full\09__350002022300997.wav |
| 09__350002022300997.wav | SPEAKER_01 | agent_candidate | 67.733 | 70.231 | 2.498 | C:\jupyter_env\VOC_full\09__350002022300997.wav |
| 09__350002022300997.wav | SPEAKER_01 | agent_candidate | 72.61 | 78.415 | 5.805 | C:\jupyter_env\VOC_full\09__350002022300997.wav |
| 09__350002022300997.wav | SPEAKER_01 | agent_candidate | 83.343 | 88.118 | 4.776 | C:\jupyter_env\VOC_full\09__350002022300997.wav |
| 09__350002022300997.wav | SPEAKER_01 | agent_candidate | 88.422 | 88.439 | 0.017 | C:\jupyter_env\VOC_full\09__350002022300997.wav |
| 09__350002022300997.wav | SPEAKER_01 | agent_candidate | 88.456 | 89.249 | 0.793 | C:\jupyter_env\VOC_full\09__350002022300997.wav |
| 09__350002022300997.wav | SPEAKER_01 | agent_candidate | 90.042 | 92.084 | 2.042 | C:\jupyter_env\VOC_full\09__350002022300997.wav |
| 09__350002022300997.wav | SPEAKER_01 | agent_candidate | 92.422 | 92.776 | 0.354 | C:\jupyter_env\VOC_full\09__350002022300997.wav |
| 09__350002022300997.wav | SPEAKER_01 | agent_candidate | 93.89 | 94.092 | 0.203 | C:\jupyter_env\VOC_full\09__350002022300997.wav |
| 09__350002022300997.wav | SPEAKER_01 | agent_candidate | 94.7 | 96.809 | 2.109 | C:\jupyter_env\VOC_full\09__350002022300997.wav |
| 09__350002022300997.wav | SPEAKER_01 | agent_candidate | 98.463 | 99.003 | 0.54 | C:\jupyter_env\VOC_full\09__350002022300997.wav |
| 09__350002022300997.wav | SPEAKER_01 | agent_candidate | 100.673 | 102.58 | 1.907 | C:\jupyter_env\VOC_full\09__350002022300997.wav |
| 09__350002022300997.wav | SPEAKER_01 | agent_candidate | 103.053 | 103.154 | 0.101 | C:\jupyter_env\VOC_full\09__350002022300997.wav |
| 09__350002022300998.wav | SPEAKER_01 | agent_candidate | 5.954 | 6.022 | 0.068 | C:\jupyter_env\VOC_full\09__350002022300998.wav |
| 09__350002022300998.wav | SPEAKER_01 | agent_candidate | 6.68 | 6.933 | 0.253 | C:\jupyter_env\VOC_full\09__350002022300998.wav |
| 09__350002022300998.wav | SPEAKER_01 | agent_candidate | 13.97 | 15.286 | 1.316 | C:\jupyter_env\VOC_full\09__350002022300998.wav |
| 09__350002022300998.wav | SPEAKER_01 | agent_candidate | 15.961 | 23.723 | 7.762 | C:\jupyter_env\VOC_full\09__350002022300998.wav |
| 09__350002022300998.wav | SPEAKER_01 | agent_candidate | 32.92 | 41.155 | 8.235 | C:\jupyter_env\VOC_full\09__350002022300998.wav |
| 09__350002022300998.wav | SPEAKER_01 | agent_candidate | 43.366 | 44.767 | 1.401 | C:\jupyter_env\VOC_full\09__350002022300998.wav |
| 09__350002022300999.wav | SPEAKER_01 | agent_candidate | 1.432 | 5.448 | 4.016 | C:\jupyter_env\VOC_full\09__350002022300999.wav |
| 09__350002022300999.wav | SPEAKER_01 | agent_candidate | 6.612 | 16.231 | 9.619 | C:\jupyter_env\VOC_full\09__350002022300999.wav |
| 09__350002022300999.wav | SPEAKER_01 | agent_candidate | 16.552 | 20.888 | 4.337 | C:\jupyter_env\VOC_full\09__350002022300999.wav |
| 09__350002022300999.wav | SPEAKER_01 | agent_candidate | 21.209 | 24.027 | 2.818 | C:\jupyter_env\VOC_full\09__350002022300999.wav |
| 09__350002022300999.wav | SPEAKER_01 | agent_candidate | 27.47 | 28.145 | 0.675 | C:\jupyter_env\VOC_full\09__350002022300999.wav |
| 09__350002022300999.wav | SPEAKER_01 | agent_candidate | 31.756 | 32.887 | 1.131 | C:\jupyter_env\VOC_full\09__350002022300999.wav |
산출물 CSV: research_continuity/25_customer_agent_interaction_v182/customer_candidate_segments_v182.csv (행 29,386, 열 7)
대용량 표 요약·앞/뒤 표본 포함, 원본은 데이터 ZIP 수록
| audio_name | speaker | role_candidate | start_sec | end_sec | duration_sec | audio_path_private |
|---|---|---|---|---|---|---|
| 00__350002022300000.wav | SPEAKER_00 | customer_candidate | 0.031 | 3.119 | 3.088 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_00 | customer_candidate | 4.283 | 4.57 | 0.287 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_00 | customer_candidate | 5.768 | 6.832 | 1.063 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_00 | customer_candidate | 10.679 | 10.797 | 0.118 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_00 | customer_candidate | 19.69 | 19.977 | 0.287 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_00 | customer_candidate | 28.313 | 28.347 | 0.034 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_00 | customer_candidate | 35.367 | 36.903 | 1.536 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_00 | customer_candidate | 40.193 | 40.936 | 0.742 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_00 | customer_candidate | 45.779 | 46.133 | 0.354 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_00 | customer_candidate | 54.25 | 54.47 | 0.219 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_00 | customer_candidate | 54.487 | 54.773 | 0.287 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_00 | customer_candidate | 78.601 | 80.896 | 2.295 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_00 | customer_candidate | 81.402 | 84.102 | 2.7 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_00 | customer_candidate | 88.203 | 89.789 | 1.586 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_00 | customer_candidate | 90.059 | 90.515 | 0.456 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_00 | customer_candidate | 106.478 | 111.288 | 4.809 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_00 | customer_candidate | 111.524 | 116.485 | 4.961 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_00 | customer_candidate | 119.202 | 119.742 | 0.54 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_00 | customer_candidate | 122.932 | 124.18 | 1.249 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_00 | customer_candidate | 124.636 | 125.024 | 0.388 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_00 | customer_candidate | 130.39 | 131.251 | 0.861 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_00 | customer_candidate | 131.572 | 132.078 | 0.506 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_00 | customer_candidate | 136.955 | 139.081 | 2.126 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_00 | customer_candidate | 145.122 | 145.628 | 0.506 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| 00__350002022300000.wav | SPEAKER_00 | customer_candidate | 149.864 | 150.472 | 0.607 | C:\jupyter_env\VOC_full\00__350002022300000.wav |
| ... 중간 29,336행은 전체 데이터 부록 ZIP에 원본으로 포함 ... | ||||||
| 09__350002022300997.wav | SPEAKER_00 | customer_candidate | 80.238 | 83.613 | 3.375 | C:\jupyter_env\VOC_full\09__350002022300997.wav |
| 09__350002022300997.wav | SPEAKER_00 | customer_candidate | 86.549 | 87.983 | 1.434 | C:\jupyter_env\VOC_full\09__350002022300997.wav |
| 09__350002022300997.wav | SPEAKER_00 | customer_candidate | 88.118 | 88.203 | 0.084 | C:\jupyter_env\VOC_full\09__350002022300997.wav |
| 09__350002022300997.wav | SPEAKER_00 | customer_candidate | 88.388 | 88.422 | 0.034 | C:\jupyter_env\VOC_full\09__350002022300997.wav |
| 09__350002022300997.wav | SPEAKER_00 | customer_candidate | 88.439 | 90.312 | 1.873 | C:\jupyter_env\VOC_full\09__350002022300997.wav |
| 09__350002022300997.wav | SPEAKER_00 | customer_candidate | 91.51 | 92.253 | 0.743 | C:\jupyter_env\VOC_full\09__350002022300997.wav |
| 09__350002022300997.wav | SPEAKER_00 | customer_candidate | 92.371 | 93.4 | 1.029 | C:\jupyter_env\VOC_full\09__350002022300997.wav |
| 09__350002022300997.wav | SPEAKER_00 | customer_candidate | 95.02 | 95.78 | 0.759 | C:\jupyter_env\VOC_full\09__350002022300997.wav |
| 09__350002022300997.wav | SPEAKER_00 | customer_candidate | 97.518 | 98.362 | 0.844 | C:\jupyter_env\VOC_full\09__350002022300997.wav |
| 09__350002022300997.wav | SPEAKER_00 | customer_candidate | 99.003 | 100.555 | 1.552 | C:\jupyter_env\VOC_full\09__350002022300997.wav |
| 09__350002022300997.wav | SPEAKER_00 | customer_candidate | 101.686 | 103.053 | 1.367 | C:\jupyter_env\VOC_full\09__350002022300997.wav |
| 09__350002022300998.wav | SPEAKER_00 | customer_candidate | 0.318 | 3.794 | 3.476 | C:\jupyter_env\VOC_full\09__350002022300998.wav |
| 09__350002022300998.wav | SPEAKER_00 | customer_candidate | 4.351 | 7.034 | 2.683 | C:\jupyter_env\VOC_full\09__350002022300998.wav |
| 09__350002022300998.wav | SPEAKER_00 | customer_candidate | 7.81 | 10.257 | 2.447 | C:\jupyter_env\VOC_full\09__350002022300998.wav |
| 09__350002022300998.wav | SPEAKER_00 | customer_candidate | 10.882 | 12.299 | 1.418 | C:\jupyter_env\VOC_full\09__350002022300998.wav |
| 09__350002022300998.wav | SPEAKER_00 | customer_candidate | 13.109 | 13.97 | 0.861 | C:\jupyter_env\VOC_full\09__350002022300998.wav |
| 09__350002022300998.wav | SPEAKER_00 | customer_candidate | 15.657 | 16.298 | 0.641 | C:\jupyter_env\VOC_full\09__350002022300998.wav |
| 09__350002022300998.wav | SPEAKER_00 | customer_candidate | 23.723 | 24.415 | 0.692 | C:\jupyter_env\VOC_full\09__350002022300998.wav |
| 09__350002022300998.wav | SPEAKER_00 | customer_candidate | 42.978 | 43.113 | 0.135 | C:\jupyter_env\VOC_full\09__350002022300998.wav |
| 09__350002022300999.wav | SPEAKER_00 | customer_candidate | 0.773 | 1.195 | 0.422 | C:\jupyter_env\VOC_full\09__350002022300999.wav |
| 09__350002022300999.wav | SPEAKER_00 | customer_candidate | 5.988 | 6.292 | 0.304 | C:\jupyter_env\VOC_full\09__350002022300999.wav |
| 09__350002022300999.wav | SPEAKER_00 | customer_candidate | 20.365 | 20.635 | 0.27 | C:\jupyter_env\VOC_full\09__350002022300999.wav |
| 09__350002022300999.wav | SPEAKER_00 | customer_candidate | 24.027 | 24.837 | 0.81 | C:\jupyter_env\VOC_full\09__350002022300999.wav |
| 09__350002022300999.wav | SPEAKER_00 | customer_candidate | 25.225 | 31.165 | 5.94 | C:\jupyter_env\VOC_full\09__350002022300999.wav |
| 09__350002022300999.wav | SPEAKER_00 | customer_candidate | 32.887 | 34.405 | 1.519 | C:\jupyter_env\VOC_full\09__350002022300999.wav |
산출물 CSV: research_continuity/25_customer_agent_interaction_v182/other_speaker_segments_v182.csv (행 0, 열 0)
전체 행 포함
산출물 CSV: research_continuity/25_customer_agent_interaction_v182/speaker0_customer_candidate_call_records_1000_v194.csv (행 1,000, 열 17)
대용량 표 요약·앞/뒤 표본 포함, 원본은 데이터 ZIP 수록
| audio_name | speaker | role_candidate | segment_count | total_speech_sec | speech_ratio_in_call | mean_segment_sec | median_segment_sec | first_start_sec | last_end_sec | side_status | detected_in_diarization | missing_reason | speaker_side_key | role_confirmed | claim_policy | unit_definition |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 00__350002022300000.wav | SPEAKER_00 | customer_candidate | 89 | 141.581 | 0.270357 | 1.590798 | 1.063 | 0.031 | 522.093 | DETECTED | Y | 00__350002022300000.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300001.wav | SPEAKER_00 | customer_candidate | 29 | 59.638 | 0.411135 | 2.056483 | 1.401 | 0.301 | 145.358 | DETECTED | Y | 00__350002022300001.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300002.wav | SPEAKER_00 | customer_candidate | 17 | 33.024 | 0.528485 | 1.942588 | 1.063 | 3.001 | 63.076 | DETECTED | Y | 00__350002022300002.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300003.wav | SPEAKER_00 | customer_candidate | 12 | 24.081 | 0.452446 | 2.00675 | 1.7385 | 1.786 | 54.082 | DETECTED | Y | 00__350002022300003.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300004.wav | SPEAKER_00 | customer_candidate | 14 | 35.707 | 0.688792 | 2.5505 | 2.472 | 1.313 | 51.922 | DETECTED | Y | 00__350002022300004.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300005.wav | SPEAKER_00 | customer_candidate | 7 | 10.664 | 0.393738 | 1.523429 | 1.755 | 2.697 | 29.275 | DETECTED | Y | 00__350002022300005.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300006.wav | SPEAKER_00 | customer_candidate | 26 | 37.276 | 0.384431 | 1.433692 | 1.139 | 0.79 | 97.012 | DETECTED | Y | 00__350002022300006.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300007.wav | SPEAKER_00 | customer_candidate | 32 | 67.232 | 0.305414 | 2.101 | 2.076 | 4.199 | 220.047 | DETECTED | Y | 00__350002022300007.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300008.wav | SPEAKER_00 | customer_candidate | 12 | 24.656 | 0.324618 | 2.054667 | 1.6455 | 2.393 | 76.036 | DETECTED | Y | 00__350002022300008.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300009.wav | SPEAKER_00 | customer_candidate | 16 | 25.819 | 0.375833 | 1.613687 | 1.451 | 0.824 | 68.999 | DETECTED | Y | 00__350002022300009.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300010.wav | SPEAKER_00 | customer_candidate | 2 | 6.294 | 0.958867 | 3.147 | 3.147 | 0.031 | 6.595 | DETECTED | Y | 00__350002022300010.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300011.wav | SPEAKER_00 | customer_candidate | 21 | 35.894 | 0.228741 | 1.709238 | 0.945 | 4.199 | 155.669 | DETECTED | Y | 00__350002022300011.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300012.wav | SPEAKER_00 | customer_candidate | 64 | 126.549 | 0.544763 | 1.977328 | 1.7635 | 1.229 | 233.53 | DETECTED | Y | 00__350002022300012.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300013.wav | SPEAKER_00 | customer_candidate | 58 | 191.377 | 0.597704 | 3.299603 | 2.143 | 0.79 | 320.977 | DETECTED | Y | 00__350002022300013.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300014.wav | SPEAKER_00 | customer_candidate | 11 | 19.811 | 0.418979 | 1.801 | 1.755 | 0.419 | 47.703 | DETECTED | Y | 00__350002022300014.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300015.wav | SPEAKER_00 | customer_candidate | 13 | 31.0 | 0.65914 | 2.384615 | 0.962 | 1.077 | 47.821 | DETECTED | Y | 00__350002022300015.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300016.wav | SPEAKER_00 | customer_candidate | 4 | 7.121 | 0.344193 | 1.78025 | 1.0885 | 1.313 | 16.484 | DETECTED | Y | 00__350002022300016.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300017.wav | SPEAKER_00 | customer_candidate | 7 | 7.948 | 0.368253 | 1.135429 | 0.709 | 1.229 | 22.103 | DETECTED | Y | 00__350002022300017.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300018.wav | SPEAKER_00 | customer_candidate | 119 | 291.259 | 0.560256 | 2.447555 | 1.924 | 1.364 | 520.642 | DETECTED | Y | 00__350002022300018.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300019.wav | SPEAKER_00 | customer_candidate | 5 | 8.269 | 0.613291 | 1.6538 | 1.198 | 1.027 | 14.037 | DETECTED | Y | 00__350002022300019.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300020.wav | SPEAKER_00 | customer_candidate | 162 | 210.332 | 0.203005 | 1.298346 | 0.861 | 0.031 | 1036.122 | DETECTED | Y | 00__350002022300020.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300021.wav | SPEAKER_00 | customer_candidate | 103 | 417.253 | 0.801651 | 4.051 | 3.392 | 1.668 | 521.097 | DETECTED | Y | 00__350002022300021.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300022.wav | SPEAKER_00 | customer_candidate | 5 | 13.787 | 0.555032 | 2.7574 | 0.878 | 2.326 | 26.693 | DETECTED | Y | 00__350002022300022.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300023.wav | SPEAKER_00 | customer_candidate | 68 | 215.477 | 0.72003 | 3.168779 | 2.6495 | 1.465 | 298.938 | DETECTED | Y | 00__350002022300023.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300024.wav | SPEAKER_00 | customer_candidate | 10 | 6.717 | 0.220035 | 0.6717 | 0.599 | 0.368 | 29.495 | DETECTED | Y | 00__350002022300024.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| ... 중간 950행은 전체 데이터 부록 ZIP에 원본으로 포함 ... | ||||||||||||||||
| 09__350002022300975.wav | SPEAKER_00 | customer_candidate | 6 | 15.846 | 0.61942 | 2.641 | 1.3585 | 6.916 | 26.018 | DETECTED | Y | 09__350002022300975.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300976.wav | SPEAKER_00 | customer_candidate | 34 | 65.137 | 0.54274 | 1.915794 | 1.7385 | 0.875 | 120.89 | DETECTED | Y | 09__350002022300976.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300977.wav | SPEAKER_00 | customer_candidate | 26 | 61.074 | 0.510034 | 2.349 | 2.4555 | 0.25 | 119.995 | DETECTED | Y | 09__350002022300977.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300978.wav | SPEAKER_00 | customer_candidate | 16 | 35.268 | 0.493266 | 2.20425 | 2.0505 | 0.605 | 72.104 | DETECTED | Y | 09__350002022300978.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300979.wav | SPEAKER_00 | customer_candidate | 2 | 4.911 | 0.941887 | 2.4555 | 2.4555 | 0.031 | 5.245 | DETECTED | Y | 09__350002022300979.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300980.wav | SPEAKER_00 | customer_candidate | 13 | 14.766 | 0.328586 | 1.135846 | 1.401 | 0.031 | 44.969 | DETECTED | Y | 09__350002022300980.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300981.wav | SPEAKER_00 | customer_candidate | 27 | 55.959 | 0.400013 | 2.072556 | 1.282 | 0.335 | 139.03 | DETECTED | Y | 09__350002022300981.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300982.wav | SPEAKER_00 | customer_candidate | 1 | 5.586 | 1.0 | 5.586 | 5.586 | 0.841 | 6.427 | DETECTED | Y | 09__350002022300982.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300983.wav | SPEAKER_00 | customer_candidate | 54 | 95.141 | 0.471759 | 1.76187 | 1.0715 | 0.588 | 202.261 | DETECTED | Y | 09__350002022300983.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300984.wav | SPEAKER_00 | customer_candidate | 1 | 5.602 | 1.0 | 5.602 | 5.602 | 0.993 | 6.595 | DETECTED | Y | 09__350002022300984.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300985.wav | SPEAKER_00 | customer_candidate | 7 | 9.247 | 0.205544 | 1.321 | 0.675 | 6.055 | 46.538 | DETECTED | Y | 09__350002022300985.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300986.wav | SPEAKER_00 | customer_candidate | 47 | 78.671 | 0.457597 | 1.673851 | 0.928 | 2.967 | 172.493 | DETECTED | Y | 09__350002022300986.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300987.wav | SPEAKER_00 | customer_candidate | 40 | 96.525 | 0.565943 | 2.413125 | 2.3795 | 0.672 | 171.228 | DETECTED | Y | 09__350002022300987.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300988.wav | SPEAKER_00 | customer_candidate | 9 | 22.46 | 0.138354 | 2.495556 | 0.641 | 26.204 | 163.752 | DETECTED | Y | 09__350002022300988.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300989.wav | SPEAKER_00 | customer_candidate | 111 | 217.061 | 0.416207 | 1.955505 | 1.401 | 0.031 | 521.452 | DETECTED | Y | 09__350002022300989.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300990.wav | SPEAKER_00 | customer_candidate | 39 | 95.46 | 0.554923 | 2.447692 | 1.654 | 0.655 | 172.679 | DETECTED | Y | 09__350002022300990.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300991.wav | SPEAKER_00 | customer_candidate | 44 | 110.209 | 0.739712 | 2.50475 | 2.371 | 0.622 | 149.611 | DETECTED | Y | 09__350002022300991.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300992.wav | SPEAKER_00 | customer_candidate | 11 | 19.862 | 0.325228 | 1.805636 | 1.89 | 0.554 | 60.899 | DETECTED | Y | 09__350002022300992.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300993.wav | SPEAKER_00 | customer_candidate | 4 | 14.26 | 0.48041 | 3.565 | 4.219 | 0.925 | 15.742 | DETECTED | Y | 09__350002022300993.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300994.wav | SPEAKER_00 | customer_candidate | 4 | 8.168 | 0.596814 | 2.042 | 1.0885 | 1.077 | 13.514 | DETECTED | Y | 09__350002022300994.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300995.wav | SPEAKER_00 | customer_candidate | 45 | 89.491 | 0.585337 | 1.988689 | 1.384 | 1.87 | 153.83 | DETECTED | Y | 09__350002022300995.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300996.wav | SPEAKER_00 | customer_candidate | 22 | 41.901 | 0.699644 | 1.904591 | 0.8355 | 0.368 | 59.92 | DETECTED | Y | 09__350002022300996.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300997.wav | SPEAKER_00 | customer_candidate | 30 | 30.391 | 0.296891 | 1.013033 | 0.8015 | 0.79 | 103.053 | DETECTED | Y | 09__350002022300997.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300998.wav | SPEAKER_00 | customer_candidate | 8 | 12.353 | 0.277914 | 1.544125 | 1.1395 | 0.318 | 43.113 | DETECTED | Y | 09__350002022300998.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300999.wav | SPEAKER_00 | customer_candidate | 6 | 9.265 | 0.275482 | 1.544167 | 0.616 | 0.773 | 34.405 | DETECTED | Y | 09__350002022300999.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file |
산출물 CSV: research_continuity/25_customer_agent_interaction_v182/speaker0_to_speaker1_response_pairs_v182.csv (행 28,149, 열 13)
대용량 표 요약·앞/뒤 표본 포함, 원본은 데이터 ZIP 수록
| audio_name | source_speaker | target_speaker | source_role_candidate | target_role_candidate | source_turn_no | source_start_sec | source_end_sec | source_duration_sec | target_start_sec | target_end_sec | target_duration_sec | response_delay_sec |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 00__350002022300000.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 1 | 0.031 | 3.119 | 3.088 | 4.267 | 7.439 | 3.172 | 1.148 |
| 00__350002022300000.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 2 | 4.283 | 4.57 | 0.287 | 7.76 | 10.679 | 2.919 | 3.19 |
| 00__350002022300000.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 3 | 5.768 | 6.832 | 1.063 | 7.76 | 10.679 | 2.919 | 0.928 |
| 00__350002022300000.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 4 | 10.679 | 10.797 | 0.118 | 10.797 | 10.932 | 0.135 | 0.0 |
| 00__350002022300000.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 5 | 19.69 | 19.977 | 0.287 | 19.977 | 20.281 | 0.304 | 0.0 |
| 00__350002022300000.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 6 | 28.313 | 28.347 | 0.034 | 29.123 | 32.718 | 3.594 | 0.776 |
| 00__350002022300000.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 7 | 35.367 | 36.903 | 1.536 | 38.354 | 38.405 | 0.051 | 1.451 |
| 00__350002022300000.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 8 | 40.193 | 40.936 | 0.742 | 45.627 | 48.04 | 2.413 | 4.691 |
| 00__350002022300000.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 9 | 45.779 | 46.133 | 0.354 | 49.593 | 54.487 | 4.894 | 3.46 |
| 00__350002022300000.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 10 | 54.25 | 54.47 | 0.219 | 54.959 | 56.714 | 1.755 | 0.489 |
| 00__350002022300000.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 11 | 54.487 | 54.773 | 0.287 | 54.959 | 56.714 | 1.755 | 0.186 |
| 00__350002022300000.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 12 | 78.601 | 80.896 | 2.295 | 82.297 | 82.668 | 0.371 | 1.401 |
| 00__350002022300000.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 13 | 81.402 | 84.102 | 2.7 | 86.887 | 88.203 | 1.316 | 2.785 |
| 00__350002022300000.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 14 | 88.203 | 89.789 | 1.586 | 89.789 | 90.059 | 0.27 | 0.0 |
| 00__350002022300000.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 15 | 90.059 | 90.515 | 0.456 | 92.573 | 98.362 | 5.788 | 2.058 |
| 00__350002022300000.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 16 | 106.478 | 111.288 | 4.809 | 117.97 | 120.957 | 2.987 | 6.682 |
| 00__350002022300000.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 17 | 111.524 | 116.485 | 4.961 | 117.97 | 120.957 | 2.987 | 1.485 |
| 00__350002022300000.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 18 | 119.202 | 119.742 | 0.54 | 121.16 | 125.21 | 4.05 | 1.418 |
| 00__350002022300000.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 19 | 122.932 | 124.18 | 1.249 | 125.345 | 129.547 | 4.202 | 1.165 |
| 00__350002022300000.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 20 | 124.636 | 125.024 | 0.388 | 125.345 | 129.547 | 4.202 | 0.321 |
| 00__350002022300000.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 21 | 130.39 | 131.251 | 0.861 | 134.305 | 136.955 | 2.649 | 3.054 |
| 00__350002022300000.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 22 | 131.572 | 132.078 | 0.506 | 134.305 | 136.955 | 2.649 | 2.227 |
| 00__350002022300000.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 23 | 136.955 | 139.081 | 2.126 | 140.566 | 143.013 | 2.447 | 1.485 |
| 00__350002022300000.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 24 | 145.122 | 145.628 | 0.506 | 147.08 | 149.746 | 2.666 | 1.452 |
| 00__350002022300000.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 25 | 149.864 | 150.472 | 0.607 | 151.94 | 153.998 | 2.059 | 1.468 |
| ... 중간 28,099행은 전체 데이터 부록 ZIP에 원본으로 포함 ... | ||||||||||||
| 09__350002022300997.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 19 | 70.99 | 71.497 | 0.506 | 72.61 | 78.415 | 5.805 | 1.113 |
| 09__350002022300997.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 20 | 80.238 | 83.613 | 3.375 | 88.422 | 88.439 | 0.017 | 4.809 |
| 09__350002022300997.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 21 | 86.549 | 87.983 | 1.434 | 88.422 | 88.439 | 0.017 | 0.439 |
| 09__350002022300997.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 22 | 88.118 | 88.203 | 0.084 | 88.422 | 88.439 | 0.017 | 0.219 |
| 09__350002022300997.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 23 | 88.388 | 88.422 | 0.034 | 88.422 | 88.439 | 0.017 | 0.0 |
| 09__350002022300997.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 24 | 88.439 | 90.312 | 1.873 | 92.422 | 92.776 | 0.354 | 2.11 |
| 09__350002022300997.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 25 | 91.51 | 92.253 | 0.743 | 92.422 | 92.776 | 0.354 | 0.169 |
| 09__350002022300997.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 26 | 92.371 | 93.4 | 1.029 | 93.89 | 94.092 | 0.203 | 0.49 |
| 09__350002022300997.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 27 | 95.02 | 95.78 | 0.759 | 98.463 | 99.003 | 0.54 | 2.683 |
| 09__350002022300997.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 28 | 97.518 | 98.362 | 0.844 | 98.463 | 99.003 | 0.54 | 0.101 |
| 09__350002022300997.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 29 | 99.003 | 100.555 | 1.552 | 100.673 | 102.58 | 1.907 | 0.118 |
| 09__350002022300997.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 30 | 101.686 | 103.053 | 1.367 | 103.053 | 103.154 | 0.101 | 0.0 |
| 09__350002022300998.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 1 | 0.318 | 3.794 | 3.476 | 5.954 | 6.022 | 0.068 | 2.16 |
| 09__350002022300998.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 2 | 4.351 | 7.034 | 2.683 | 13.97 | 15.286 | 1.316 | 6.936 |
| 09__350002022300998.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 3 | 7.81 | 10.257 | 2.447 | 13.97 | 15.286 | 1.316 | 3.713 |
| 09__350002022300998.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 4 | 10.882 | 12.299 | 1.418 | 13.97 | 15.286 | 1.316 | 1.671 |
| 09__350002022300998.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 5 | 13.109 | 13.97 | 0.861 | 13.97 | 15.286 | 1.316 | 0.0 |
| 09__350002022300998.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 6 | 15.657 | 16.298 | 0.641 | 32.92 | 41.155 | 8.235 | 16.622 |
| 09__350002022300998.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 7 | 23.723 | 24.415 | 0.692 | 32.92 | 41.155 | 8.235 | 8.505 |
| 09__350002022300998.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 8 | 42.978 | 43.113 | 0.135 | 43.366 | 44.767 | 1.401 | 0.253 |
| 09__350002022300999.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 1 | 0.773 | 1.195 | 0.422 | 1.432 | 5.448 | 4.016 | 0.237 |
| 09__350002022300999.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 2 | 5.988 | 6.292 | 0.304 | 6.612 | 16.231 | 9.619 | 0.32 |
| 09__350002022300999.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 3 | 20.365 | 20.635 | 0.27 | 21.209 | 24.027 | 2.818 | 0.574 |
| 09__350002022300999.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 4 | 24.027 | 24.837 | 0.81 | 27.47 | 28.145 | 0.675 | 2.633 |
| 09__350002022300999.wav | SPEAKER_00 | SPEAKER_01 | customer_candidate | agent_candidate | 5 | 25.225 | 31.165 | 5.94 | 31.756 | 32.887 | 1.131 | 0.591 |
산출물 CSV: research_continuity/25_customer_agent_interaction_v182/speaker1_agent_candidate_call_records_1000_v194.csv (행 1,000, 열 17)
대용량 표 요약·앞/뒤 표본 포함, 원본은 데이터 ZIP 수록
| audio_name | speaker | role_candidate | segment_count | total_speech_sec | speech_ratio_in_call | mean_segment_sec | median_segment_sec | first_start_sec | last_end_sec | side_status | detected_in_diarization | missing_reason | speaker_side_key | role_confirmed | claim_policy | unit_definition |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 00__350002022300000.wav | SPEAKER_01 | agent_candidate | 105 | 189.085 | 0.361068 | 1.80081 | 0.861 | 4.267 | 523.713 | DETECTED | Y | 00__350002022300000.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300001.wav | SPEAKER_01 | agent_candidate | 32 | 67.567 | 0.465796 | 2.111469 | 1.7635 | 3.001 | 144.515 | DETECTED | Y | 00__350002022300001.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300002.wav | SPEAKER_01 | agent_candidate | 19 | 17.432 | 0.278966 | 0.917474 | 0.726 | 0.588 | 62.502 | DETECTED | Y | 00__350002022300002.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300003.wav | SPEAKER_01 | agent_candidate | 18 | 22.645 | 0.425466 | 1.258056 | 1.2655 | 0.858 | 50.74 | DETECTED | Y | 00__350002022300003.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300004.wav | SPEAKER_01 | agent_candidate | 17 | 19.185 | 0.370081 | 1.128529 | 0.742 | 0.082 | 50.437 | DETECTED | Y | 00__350002022300004.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300005.wav | SPEAKER_01 | agent_candidate | 6 | 17.112 | 0.631812 | 2.852 | 2.5905 | 2.191 | 29.191 | DETECTED | Y | 00__350002022300005.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300006.wav | SPEAKER_01 | agent_candidate | 27 | 33.682 | 0.347366 | 1.247481 | 0.81 | 3.102 | 97.754 | DETECTED | Y | 00__350002022300006.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300007.wav | SPEAKER_01 | agent_candidate | 31 | 58.944 | 0.267764 | 1.901419 | 1.181 | 0.031 | 220.165 | DETECTED | Y | 00__350002022300007.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300008.wav | SPEAKER_01 | agent_candidate | 12 | 26.916 | 0.354372 | 2.243 | 1.8395 | 0.082 | 75.547 | DETECTED | Y | 00__350002022300008.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300009.wav | SPEAKER_01 | agent_candidate | 19 | 26.106 | 0.380011 | 1.374 | 1.198 | 4.705 | 69.522 | DETECTED | Y | 00__350002022300009.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300010.wav | SPEAKER_01 | agent_candidate | 0 | 0 | 0 | MISSING_NO_SEGMENTS | N | No segment for this speaker label in pyannote output; kept as zero-slot for 1,000 x 2 audit balance | 00__350002022300010.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | |||||
| 00__350002022300011.wav | SPEAKER_01 | agent_candidate | 28 | 41.734 | 0.265957 | 1.4905 | 0.8355 | 1.162 | 158.082 | DETECTED | Y | 00__350002022300011.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300012.wav | SPEAKER_01 | agent_candidate | 33 | 86.742 | 0.373403 | 2.628545 | 2.481 | 1.246 | 232.585 | DETECTED | Y | 00__350002022300012.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300013.wav | SPEAKER_01 | agent_candidate | 92 | 134.245 | 0.419271 | 1.459185 | 0.962 | 2.326 | 320.538 | DETECTED | Y | 00__350002022300013.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300014.wav | SPEAKER_01 | agent_candidate | 12 | 11.729 | 0.248054 | 0.977417 | 0.827 | 2.798 | 45.813 | DETECTED | Y | 00__350002022300014.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300015.wav | SPEAKER_01 | agent_candidate | 6 | 17.397 | 0.369905 | 2.8995 | 2.894 | 10.207 | 48.108 | DETECTED | Y | 00__350002022300015.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300016.wav | SPEAKER_01 | agent_candidate | 9 | 9.367 | 0.452753 | 1.040778 | 0.945 | 1.263 | 21.952 | DETECTED | Y | 00__350002022300016.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300017.wav | SPEAKER_01 | agent_candidate | 5 | 13.315 | 0.616921 | 2.663 | 1.519 | 8.03 | 22.812 | DETECTED | Y | 00__350002022300017.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300018.wav | SPEAKER_01 | agent_candidate | 108 | 113.851 | 0.219 | 1.054176 | 0.7505 | 5.144 | 521.232 | DETECTED | Y | 00__350002022300018.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300019.wav | SPEAKER_01 | agent_candidate | 5 | 5.855 | 0.434251 | 1.171 | 0.624 | 0.554 | 13.328 | DETECTED | Y | 00__350002022300019.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300020.wav | SPEAKER_01 | agent_candidate | 215 | 754.177 | 0.727906 | 3.5078 | 2.7 | 1.162 | 1035.38 | DETECTED | Y | 00__350002022300020.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300021.wav | SPEAKER_01 | agent_candidate | 75 | 80.611 | 0.154875 | 1.074813 | 0.776 | 0.605 | 516.558 | DETECTED | Y | 00__350002022300021.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300022.wav | SPEAKER_01 | agent_candidate | 7 | 16.217 | 0.652858 | 2.316714 | 0.793 | 1.853 | 26.339 | DETECTED | Y | 00__350002022300022.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300023.wav | SPEAKER_01 | agent_candidate | 51 | 47.759 | 0.15959 | 0.936451 | 0.709 | 0.352 | 299.613 | DETECTED | Y | 00__350002022300023.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300024.wav | SPEAKER_01 | agent_candidate | 9 | 25.092 | 0.821961 | 2.788 | 2.683 | 1.381 | 30.895 | DETECTED | Y | 00__350002022300024.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| ... 중간 950행은 전체 데이터 부록 ZIP에 원본으로 포함 ... | ||||||||||||||||
| 09__350002022300975.wav | SPEAKER_01 | agent_candidate | 5 | 10.261 | 0.401102 | 2.0522 | 2.076 | 0.436 | 21.597 | DETECTED | Y | 09__350002022300975.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300976.wav | SPEAKER_01 | agent_candidate | 21 | 23.067 | 0.192201 | 1.098429 | 0.962 | 3.676 | 119.067 | DETECTED | Y | 09__350002022300976.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300977.wav | SPEAKER_01 | agent_candidate | 27 | 34.83 | 0.290868 | 1.29 | 1.232 | 5.161 | 119.978 | DETECTED | Y | 09__350002022300977.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300978.wav | SPEAKER_01 | agent_candidate | 19 | 25.112 | 0.351222 | 1.321684 | 0.759 | 2.039 | 71.615 | DETECTED | Y | 09__350002022300978.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300979.wav | SPEAKER_01 | agent_candidate | 0 | 0 | 0 | MISSING_NO_SEGMENTS | N | No segment for this speaker label in pyannote output; kept as zero-slot for 1,000 x 2 audit balance | 09__350002022300979.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | |||||
| 09__350002022300980.wav | SPEAKER_01 | agent_candidate | 15 | 28.857 | 0.642151 | 1.9238 | 1.282 | 2.579 | 43.552 | DETECTED | Y | 09__350002022300980.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300981.wav | SPEAKER_01 | agent_candidate | 33 | 46.796 | 0.334513 | 1.418061 | 1.164 | 2.647 | 140.228 | DETECTED | Y | 09__350002022300981.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300982.wav | SPEAKER_01 | agent_candidate | 0 | 0 | 0 | MISSING_NO_SEGMENTS | N | No segment for this speaker label in pyannote output; kept as zero-slot for 1,000 x 2 audit balance | 09__350002022300982.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | |||||
| 09__350002022300983.wav | SPEAKER_01 | agent_candidate | 50 | 59.064 | 0.29287 | 1.18128 | 0.8355 | 5.448 | 202.193 | DETECTED | Y | 09__350002022300983.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300984.wav | SPEAKER_01 | agent_candidate | 0 | 0 | 0 | MISSING_NO_SEGMENTS | N | No segment for this speaker label in pyannote output; kept as zero-slot for 1,000 x 2 audit balance | 09__350002022300984.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | |||||
| 09__350002022300985.wav | SPEAKER_01 | agent_candidate | 11 | 35.067 | 0.779475 | 3.187909 | 3.341 | 1.55 | 45.897 | DETECTED | Y | 09__350002022300985.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300986.wav | SPEAKER_01 | agent_candidate | 36 | 95.293 | 0.55428 | 2.647028 | 2.295 | 0.571 | 171.92 | DETECTED | Y | 09__350002022300986.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300987.wav | SPEAKER_01 | agent_candidate | 28 | 50.706 | 0.297298 | 1.810929 | 1.409 | 4.064 | 169.169 | DETECTED | Y | 09__350002022300987.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300988.wav | SPEAKER_01 | agent_candidate | 49 | 101.856 | 0.627436 | 2.078694 | 1.738 | 1.415 | 161.693 | DETECTED | Y | 09__350002022300988.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300989.wav | SPEAKER_01 | agent_candidate | 89 | 237.939 | 0.45624 | 2.673472 | 2.228 | 1.111 | 521.553 | DETECTED | Y | 09__350002022300989.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300990.wav | SPEAKER_01 | agent_candidate | 28 | 25.463 | 0.14802 | 0.909393 | 0.5485 | 9.093 | 172.392 | DETECTED | Y | 09__350002022300990.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300991.wav | SPEAKER_01 | agent_candidate | 16 | 29.634 | 0.198901 | 1.852125 | 0.768 | 1.938 | 147.974 | DETECTED | Y | 09__350002022300991.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300992.wav | SPEAKER_01 | agent_candidate | 15 | 23.729 | 0.388548 | 1.581933 | 1.519 | 3.322 | 61.625 | DETECTED | Y | 09__350002022300992.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300993.wav | SPEAKER_01 | agent_candidate | 6 | 16.841 | 0.567362 | 2.806833 | 0.9365 | 0.031 | 29.714 | DETECTED | Y | 09__350002022300993.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300994.wav | SPEAKER_01 | agent_candidate | 2 | 6.261 | 0.457475 | 3.1305 | 3.1305 | 8.165 | 14.763 | DETECTED | Y | 09__350002022300994.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300995.wav | SPEAKER_01 | agent_candidate | 34 | 55.691 | 0.36426 | 1.637971 | 0.5315 | 9.278 | 154.758 | DETECTED | Y | 09__350002022300995.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300996.wav | SPEAKER_01 | agent_candidate | 23 | 23.676 | 0.395331 | 1.029391 | 0.422 | 0.031 | 58.385 | DETECTED | Y | 09__350002022300996.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300997.wav | SPEAKER_01 | agent_candidate | 29 | 65.105 | 0.636015 | 2.245 | 1.35 | 2.242 | 103.154 | DETECTED | Y | 09__350002022300997.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300998.wav | SPEAKER_01 | agent_candidate | 6 | 19.035 | 0.428244 | 3.1725 | 1.3585 | 5.954 | 44.767 | DETECTED | Y | 09__350002022300998.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300999.wav | SPEAKER_01 | agent_candidate | 6 | 22.596 | 0.67186 | 3.766 | 3.417 | 1.432 | 32.887 | DETECTED | Y | 09__350002022300999.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file |
산출물 CSV: research_continuity/25_customer_agent_interaction_v182/speaker_customer_agent_call_wide_v182.csv (행 1,000, 열 13)
대용량 표 요약·앞/뒤 표본 포함, 원본은 데이터 ZIP 수록
| audio_name | speaker_count | call_start_sec | call_end_sec | call_duration_sec | speaker0_segments | speaker0_speech_sec | speaker0_speech_ratio | speaker0_mean_segment_sec | speaker1_segments | speaker1_speech_sec | speaker1_speech_ratio | speaker1_mean_segment_sec |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 00__350002022300000.wav | 2 | 0.031 | 523.713 | 523.682 | 89 | 141.581 | 0.270357 | 1.590798 | 105 | 189.085 | 0.361068 | 1.80081 |
| 00__350002022300001.wav | 2 | 0.301 | 145.358 | 145.057 | 29 | 59.638 | 0.411135 | 2.056483 | 32 | 67.567 | 0.465796 | 2.111469 |
| 00__350002022300002.wav | 2 | 0.588 | 63.076 | 62.488 | 17 | 33.024 | 0.528485 | 1.942588 | 19 | 17.432 | 0.278966 | 0.917474 |
| 00__350002022300003.wav | 2 | 0.858 | 54.082 | 53.224 | 12 | 24.081 | 0.452446 | 2.00675 | 18 | 22.645 | 0.425466 | 1.258056 |
| 00__350002022300004.wav | 2 | 0.082 | 51.922 | 51.84 | 14 | 35.707 | 0.688792 | 2.5505 | 17 | 19.185 | 0.370081 | 1.128529 |
| 00__350002022300005.wav | 2 | 2.191 | 29.275 | 27.084 | 7 | 10.664 | 0.393738 | 1.523429 | 6 | 17.112 | 0.631812 | 2.852 |
| 00__350002022300006.wav | 2 | 0.79 | 97.754 | 96.964 | 26 | 37.276 | 0.384431 | 1.433692 | 27 | 33.682 | 0.347366 | 1.247481 |
| 00__350002022300007.wav | 2 | 0.031 | 220.165 | 220.134 | 32 | 67.232 | 0.305414 | 2.101 | 31 | 58.944 | 0.267764 | 1.901419 |
| 00__350002022300008.wav | 2 | 0.082 | 76.036 | 75.954 | 12 | 24.656 | 0.324618 | 2.054667 | 12 | 26.916 | 0.354372 | 2.243 |
| 00__350002022300009.wav | 2 | 0.824 | 69.522 | 68.698 | 16 | 25.819 | 0.375833 | 1.613687 | 19 | 26.106 | 0.380011 | 1.374 |
| 00__350002022300010.wav | 1 | 0.031 | 6.595 | 6.564 | 2 | 6.294 | 0.958867 | 3.147 | ||||
| 00__350002022300011.wav | 2 | 1.162 | 158.082 | 156.92 | 21 | 35.894 | 0.228741 | 1.709238 | 28 | 41.734 | 0.265957 | 1.4905 |
| 00__350002022300012.wav | 2 | 1.229 | 233.53 | 232.301 | 64 | 126.549 | 0.544763 | 1.977328 | 33 | 86.742 | 0.373403 | 2.628545 |
| 00__350002022300013.wav | 2 | 0.79 | 320.977 | 320.187 | 58 | 191.377 | 0.597704 | 3.299603 | 92 | 134.245 | 0.419271 | 1.459185 |
| 00__350002022300014.wav | 2 | 0.419 | 47.703 | 47.284 | 11 | 19.811 | 0.418979 | 1.801 | 12 | 11.729 | 0.248054 | 0.977417 |
| 00__350002022300015.wav | 2 | 1.077 | 48.108 | 47.031 | 13 | 31.0 | 0.65914 | 2.384615 | 6 | 17.397 | 0.369905 | 2.8995 |
| 00__350002022300016.wav | 2 | 1.263 | 21.952 | 20.689 | 4 | 7.121 | 0.344193 | 1.78025 | 9 | 9.367 | 0.452753 | 1.040778 |
| 00__350002022300017.wav | 2 | 1.229 | 22.812 | 21.583 | 7 | 7.948 | 0.368253 | 1.135429 | 5 | 13.315 | 0.616921 | 2.663 |
| 00__350002022300018.wav | 2 | 1.364 | 521.232 | 519.868 | 119 | 291.259 | 0.560256 | 2.447555 | 108 | 113.851 | 0.219 | 1.054176 |
| 00__350002022300019.wav | 2 | 0.554 | 14.037 | 13.483 | 5 | 8.269 | 0.613291 | 1.6538 | 5 | 5.855 | 0.434251 | 1.171 |
| 00__350002022300020.wav | 2 | 0.031 | 1036.122 | 1036.091 | 162 | 210.332 | 0.203005 | 1.298346 | 215 | 754.177 | 0.727906 | 3.5078 |
| 00__350002022300021.wav | 2 | 0.605 | 521.097 | 520.492 | 103 | 417.253 | 0.801651 | 4.051 | 75 | 80.611 | 0.154875 | 1.074813 |
| 00__350002022300022.wav | 2 | 1.853 | 26.693 | 24.84 | 5 | 13.787 | 0.555032 | 2.7574 | 7 | 16.217 | 0.652858 | 2.316714 |
| 00__350002022300023.wav | 2 | 0.352 | 299.613 | 299.261 | 68 | 215.477 | 0.72003 | 3.168779 | 51 | 47.759 | 0.15959 | 0.936451 |
| 00__350002022300024.wav | 2 | 0.368 | 30.895 | 30.527 | 10 | 6.717 | 0.220035 | 0.6717 | 9 | 25.092 | 0.821961 | 2.788 |
| ... 중간 950행은 전체 데이터 부록 ZIP에 원본으로 포함 ... | ||||||||||||
| 09__350002022300975.wav | 2 | 0.436 | 26.018 | 25.582 | 6 | 15.846 | 0.61942 | 2.641 | 5 | 10.261 | 0.401102 | 2.0522 |
| 09__350002022300976.wav | 2 | 0.875 | 120.89 | 120.015 | 34 | 65.137 | 0.54274 | 1.915794 | 21 | 23.067 | 0.192201 | 1.098429 |
| 09__350002022300977.wav | 2 | 0.25 | 119.995 | 119.745 | 26 | 61.074 | 0.510034 | 2.349 | 27 | 34.83 | 0.290868 | 1.29 |
| 09__350002022300978.wav | 2 | 0.605 | 72.104 | 71.499 | 16 | 35.268 | 0.493266 | 2.20425 | 19 | 25.112 | 0.351222 | 1.321684 |
| 09__350002022300979.wav | 1 | 0.031 | 5.245 | 5.214 | 2 | 4.911 | 0.941887 | 2.4555 | ||||
| 09__350002022300980.wav | 2 | 0.031 | 44.969 | 44.938 | 13 | 14.766 | 0.328586 | 1.135846 | 15 | 28.857 | 0.642151 | 1.9238 |
| 09__350002022300981.wav | 2 | 0.335 | 140.228 | 139.893 | 27 | 55.959 | 0.400013 | 2.072556 | 33 | 46.796 | 0.334513 | 1.418061 |
| 09__350002022300982.wav | 1 | 0.841 | 6.427 | 5.586 | 1 | 5.586 | 1.0 | 5.586 | ||||
| 09__350002022300983.wav | 2 | 0.588 | 202.261 | 201.673 | 54 | 95.141 | 0.471759 | 1.76187 | 50 | 59.064 | 0.29287 | 1.18128 |
| 09__350002022300984.wav | 1 | 0.993 | 6.595 | 5.602 | 1 | 5.602 | 1.0 | 5.602 | ||||
| 09__350002022300985.wav | 2 | 1.55 | 46.538 | 44.988 | 7 | 9.247 | 0.205544 | 1.321 | 11 | 35.067 | 0.779475 | 3.187909 |
| 09__350002022300986.wav | 2 | 0.571 | 172.493 | 171.922 | 47 | 78.671 | 0.457597 | 1.673851 | 36 | 95.293 | 0.55428 | 2.647028 |
| 09__350002022300987.wav | 2 | 0.672 | 171.228 | 170.556 | 40 | 96.525 | 0.565943 | 2.413125 | 28 | 50.706 | 0.297298 | 1.810929 |
| 09__350002022300988.wav | 2 | 1.415 | 163.752 | 162.337 | 9 | 22.46 | 0.138354 | 2.495556 | 49 | 101.856 | 0.627436 | 2.078694 |
| 09__350002022300989.wav | 2 | 0.031 | 521.553 | 521.522 | 111 | 217.061 | 0.416207 | 1.955505 | 89 | 237.939 | 0.45624 | 2.673472 |
| 09__350002022300990.wav | 2 | 0.655 | 172.679 | 172.024 | 39 | 95.46 | 0.554923 | 2.447692 | 28 | 25.463 | 0.14802 | 0.909393 |
| 09__350002022300991.wav | 2 | 0.622 | 149.611 | 148.989 | 44 | 110.209 | 0.739712 | 2.50475 | 16 | 29.634 | 0.198901 | 1.852125 |
| 09__350002022300992.wav | 2 | 0.554 | 61.625 | 61.071 | 11 | 19.862 | 0.325228 | 1.805636 | 15 | 23.729 | 0.388548 | 1.581933 |
| 09__350002022300993.wav | 2 | 0.031 | 29.714 | 29.683 | 4 | 14.26 | 0.48041 | 3.565 | 6 | 16.841 | 0.567362 | 2.806833 |
| 09__350002022300994.wav | 2 | 1.077 | 14.763 | 13.686 | 4 | 8.168 | 0.596814 | 2.042 | 2 | 6.261 | 0.457475 | 3.1305 |
| 09__350002022300995.wav | 2 | 1.87 | 154.758 | 152.888 | 45 | 89.491 | 0.585337 | 1.988689 | 34 | 55.691 | 0.36426 | 1.637971 |
| 09__350002022300996.wav | 2 | 0.031 | 59.92 | 59.889 | 22 | 41.901 | 0.699644 | 1.904591 | 23 | 23.676 | 0.395331 | 1.029391 |
| 09__350002022300997.wav | 2 | 0.79 | 103.154 | 102.364 | 30 | 30.391 | 0.296891 | 1.013033 | 29 | 65.105 | 0.636015 | 2.245 |
| 09__350002022300998.wav | 2 | 0.318 | 44.767 | 44.449 | 8 | 12.353 | 0.277914 | 1.544125 | 6 | 19.035 | 0.428244 | 3.1725 |
| 09__350002022300999.wav | 2 | 0.773 | 34.405 | 33.632 | 6 | 9.265 | 0.275482 | 1.544167 | 6 | 22.596 | 0.67186 | 3.766 |
산출물 CSV: research_continuity/25_customer_agent_interaction_v182/speaker_role_mapping_template_v182.csv (행 2,000, 열 6)
대용량 표 요약·앞/뒤 표본 포함, 원본은 데이터 ZIP 수록
| audio_name | speaker | role_candidate | role_confirmed | confidence | evidence_note |
|---|---|---|---|---|---|
| 00__350002022300000.wav | SPEAKER_00 | customer_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_00을 고객 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 00__350002022300000.wav | SPEAKER_01 | agent_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_01을 상담사 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 00__350002022300001.wav | SPEAKER_00 | customer_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_00을 고객 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 00__350002022300001.wav | SPEAKER_01 | agent_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_01을 상담사 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 00__350002022300002.wav | SPEAKER_00 | customer_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_00을 고객 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 00__350002022300002.wav | SPEAKER_01 | agent_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_01을 상담사 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 00__350002022300003.wav | SPEAKER_00 | customer_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_00을 고객 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 00__350002022300003.wav | SPEAKER_01 | agent_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_01을 상담사 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 00__350002022300004.wav | SPEAKER_00 | customer_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_00을 고객 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 00__350002022300004.wav | SPEAKER_01 | agent_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_01을 상담사 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 00__350002022300005.wav | SPEAKER_00 | customer_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_00을 고객 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 00__350002022300005.wav | SPEAKER_01 | agent_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_01을 상담사 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 00__350002022300006.wav | SPEAKER_00 | customer_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_00을 고객 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 00__350002022300006.wav | SPEAKER_01 | agent_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_01을 상담사 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 00__350002022300007.wav | SPEAKER_00 | customer_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_00을 고객 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 00__350002022300007.wav | SPEAKER_01 | agent_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_01을 상담사 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 00__350002022300008.wav | SPEAKER_00 | customer_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_00을 고객 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 00__350002022300008.wav | SPEAKER_01 | agent_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_01을 상담사 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 00__350002022300009.wav | SPEAKER_00 | customer_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_00을 고객 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 00__350002022300009.wav | SPEAKER_01 | agent_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_01을 상담사 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 00__350002022300010.wav | SPEAKER_00 | customer_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_00을 고객 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 00__350002022300010.wav | SPEAKER_01 | agent_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_01을 상담사 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 00__350002022300011.wav | SPEAKER_00 | customer_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_00을 고객 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 00__350002022300011.wav | SPEAKER_01 | agent_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_01을 상담사 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 00__350002022300012.wav | SPEAKER_00 | customer_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_00을 고객 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| ... 중간 1,950행은 전체 데이터 부록 ZIP에 원본으로 포함 ... | |||||
| 09__350002022300987.wav | SPEAKER_01 | agent_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_01을 상담사 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 09__350002022300988.wav | SPEAKER_00 | customer_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_00을 고객 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 09__350002022300988.wav | SPEAKER_01 | agent_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_01을 상담사 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 09__350002022300989.wav | SPEAKER_00 | customer_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_00을 고객 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 09__350002022300989.wav | SPEAKER_01 | agent_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_01을 상담사 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 09__350002022300990.wav | SPEAKER_00 | customer_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_00을 고객 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 09__350002022300990.wav | SPEAKER_01 | agent_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_01을 상담사 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 09__350002022300991.wav | SPEAKER_00 | customer_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_00을 고객 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 09__350002022300991.wav | SPEAKER_01 | agent_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_01을 상담사 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 09__350002022300992.wav | SPEAKER_00 | customer_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_00을 고객 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 09__350002022300992.wav | SPEAKER_01 | agent_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_01을 상담사 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 09__350002022300993.wav | SPEAKER_00 | customer_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_00을 고객 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 09__350002022300993.wav | SPEAKER_01 | agent_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_01을 상담사 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 09__350002022300994.wav | SPEAKER_00 | customer_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_00을 고객 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 09__350002022300994.wav | SPEAKER_01 | agent_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_01을 상담사 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 09__350002022300995.wav | SPEAKER_00 | customer_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_00을 고객 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 09__350002022300995.wav | SPEAKER_01 | agent_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_01을 상담사 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 09__350002022300996.wav | SPEAKER_00 | customer_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_00을 고객 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 09__350002022300996.wav | SPEAKER_01 | agent_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_01을 상담사 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 09__350002022300997.wav | SPEAKER_00 | customer_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_00을 고객 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 09__350002022300997.wav | SPEAKER_01 | agent_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_01을 상담사 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 09__350002022300998.wav | SPEAKER_00 | customer_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_00을 고객 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 09__350002022300998.wav | SPEAKER_01 | agent_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_01을 상담사 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 09__350002022300999.wav | SPEAKER_00 | customer_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_00을 고객 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 | |
| 09__350002022300999.wav | SPEAKER_01 | agent_candidate | NEEDS_MANUAL_VALIDATION | 초기 가정: SPEAKER_01을 상담사 후보로 두되, 실제 상담 시작 멘트/STT/청취 검증 필요 |
산출물 CSV: research_continuity/25_customer_agent_interaction_v182/speaker_side_candidate_records_2000_v194.csv (행 2,000, 열 17)
대용량 표 요약·앞/뒤 표본 포함, 원본은 데이터 ZIP 수록
| audio_name | speaker | role_candidate | segment_count | total_speech_sec | speech_ratio_in_call | mean_segment_sec | median_segment_sec | first_start_sec | last_end_sec | side_status | detected_in_diarization | missing_reason | speaker_side_key | role_confirmed | claim_policy | unit_definition |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 00__350002022300000.wav | SPEAKER_00 | customer_candidate | 89 | 141.581 | 0.270357 | 1.590798 | 1.063 | 0.031 | 522.093 | DETECTED | Y | 00__350002022300000.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300000.wav | SPEAKER_01 | agent_candidate | 105 | 189.085 | 0.361068 | 1.80081 | 0.861 | 4.267 | 523.713 | DETECTED | Y | 00__350002022300000.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300001.wav | SPEAKER_00 | customer_candidate | 29 | 59.638 | 0.411135 | 2.056483 | 1.401 | 0.301 | 145.358 | DETECTED | Y | 00__350002022300001.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300001.wav | SPEAKER_01 | agent_candidate | 32 | 67.567 | 0.465796 | 2.111469 | 1.7635 | 3.001 | 144.515 | DETECTED | Y | 00__350002022300001.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300002.wav | SPEAKER_00 | customer_candidate | 17 | 33.024 | 0.528485 | 1.942588 | 1.063 | 3.001 | 63.076 | DETECTED | Y | 00__350002022300002.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300002.wav | SPEAKER_01 | agent_candidate | 19 | 17.432 | 0.278966 | 0.917474 | 0.726 | 0.588 | 62.502 | DETECTED | Y | 00__350002022300002.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300003.wav | SPEAKER_00 | customer_candidate | 12 | 24.081 | 0.452446 | 2.00675 | 1.7385 | 1.786 | 54.082 | DETECTED | Y | 00__350002022300003.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300003.wav | SPEAKER_01 | agent_candidate | 18 | 22.645 | 0.425466 | 1.258056 | 1.2655 | 0.858 | 50.74 | DETECTED | Y | 00__350002022300003.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300004.wav | SPEAKER_00 | customer_candidate | 14 | 35.707 | 0.688792 | 2.5505 | 2.472 | 1.313 | 51.922 | DETECTED | Y | 00__350002022300004.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300004.wav | SPEAKER_01 | agent_candidate | 17 | 19.185 | 0.370081 | 1.128529 | 0.742 | 0.082 | 50.437 | DETECTED | Y | 00__350002022300004.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300005.wav | SPEAKER_00 | customer_candidate | 7 | 10.664 | 0.393738 | 1.523429 | 1.755 | 2.697 | 29.275 | DETECTED | Y | 00__350002022300005.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300005.wav | SPEAKER_01 | agent_candidate | 6 | 17.112 | 0.631812 | 2.852 | 2.5905 | 2.191 | 29.191 | DETECTED | Y | 00__350002022300005.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300006.wav | SPEAKER_00 | customer_candidate | 26 | 37.276 | 0.384431 | 1.433692 | 1.139 | 0.79 | 97.012 | DETECTED | Y | 00__350002022300006.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300006.wav | SPEAKER_01 | agent_candidate | 27 | 33.682 | 0.347366 | 1.247481 | 0.81 | 3.102 | 97.754 | DETECTED | Y | 00__350002022300006.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300007.wav | SPEAKER_00 | customer_candidate | 32 | 67.232 | 0.305414 | 2.101 | 2.076 | 4.199 | 220.047 | DETECTED | Y | 00__350002022300007.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300007.wav | SPEAKER_01 | agent_candidate | 31 | 58.944 | 0.267764 | 1.901419 | 1.181 | 0.031 | 220.165 | DETECTED | Y | 00__350002022300007.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300008.wav | SPEAKER_00 | customer_candidate | 12 | 24.656 | 0.324618 | 2.054667 | 1.6455 | 2.393 | 76.036 | DETECTED | Y | 00__350002022300008.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300008.wav | SPEAKER_01 | agent_candidate | 12 | 26.916 | 0.354372 | 2.243 | 1.8395 | 0.082 | 75.547 | DETECTED | Y | 00__350002022300008.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300009.wav | SPEAKER_00 | customer_candidate | 16 | 25.819 | 0.375833 | 1.613687 | 1.451 | 0.824 | 68.999 | DETECTED | Y | 00__350002022300009.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300009.wav | SPEAKER_01 | agent_candidate | 19 | 26.106 | 0.380011 | 1.374 | 1.198 | 4.705 | 69.522 | DETECTED | Y | 00__350002022300009.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300010.wav | SPEAKER_00 | customer_candidate | 2 | 6.294 | 0.958867 | 3.147 | 3.147 | 0.031 | 6.595 | DETECTED | Y | 00__350002022300010.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300010.wav | SPEAKER_01 | agent_candidate | 0 | 0 | 0 | MISSING_NO_SEGMENTS | N | No segment for this speaker label in pyannote output; kept as zero-slot for 1,000 x 2 audit balance | 00__350002022300010.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | |||||
| 00__350002022300011.wav | SPEAKER_00 | customer_candidate | 21 | 35.894 | 0.228741 | 1.709238 | 0.945 | 4.199 | 155.669 | DETECTED | Y | 00__350002022300011.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300011.wav | SPEAKER_01 | agent_candidate | 28 | 41.734 | 0.265957 | 1.4905 | 0.8355 | 1.162 | 158.082 | DETECTED | Y | 00__350002022300011.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 00__350002022300012.wav | SPEAKER_00 | customer_candidate | 64 | 126.549 | 0.544763 | 1.977328 | 1.7635 | 1.229 | 233.53 | DETECTED | Y | 00__350002022300012.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| ... 중간 1,950행은 전체 데이터 부록 ZIP에 원본으로 포함 ... | ||||||||||||||||
| 09__350002022300987.wav | SPEAKER_01 | agent_candidate | 28 | 50.706 | 0.297298 | 1.810929 | 1.409 | 4.064 | 169.169 | DETECTED | Y | 09__350002022300987.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300988.wav | SPEAKER_00 | customer_candidate | 9 | 22.46 | 0.138354 | 2.495556 | 0.641 | 26.204 | 163.752 | DETECTED | Y | 09__350002022300988.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300988.wav | SPEAKER_01 | agent_candidate | 49 | 101.856 | 0.627436 | 2.078694 | 1.738 | 1.415 | 161.693 | DETECTED | Y | 09__350002022300988.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300989.wav | SPEAKER_00 | customer_candidate | 111 | 217.061 | 0.416207 | 1.955505 | 1.401 | 0.031 | 521.452 | DETECTED | Y | 09__350002022300989.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300989.wav | SPEAKER_01 | agent_candidate | 89 | 237.939 | 0.45624 | 2.673472 | 2.228 | 1.111 | 521.553 | DETECTED | Y | 09__350002022300989.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300990.wav | SPEAKER_00 | customer_candidate | 39 | 95.46 | 0.554923 | 2.447692 | 1.654 | 0.655 | 172.679 | DETECTED | Y | 09__350002022300990.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300990.wav | SPEAKER_01 | agent_candidate | 28 | 25.463 | 0.14802 | 0.909393 | 0.5485 | 9.093 | 172.392 | DETECTED | Y | 09__350002022300990.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300991.wav | SPEAKER_00 | customer_candidate | 44 | 110.209 | 0.739712 | 2.50475 | 2.371 | 0.622 | 149.611 | DETECTED | Y | 09__350002022300991.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300991.wav | SPEAKER_01 | agent_candidate | 16 | 29.634 | 0.198901 | 1.852125 | 0.768 | 1.938 | 147.974 | DETECTED | Y | 09__350002022300991.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300992.wav | SPEAKER_00 | customer_candidate | 11 | 19.862 | 0.325228 | 1.805636 | 1.89 | 0.554 | 60.899 | DETECTED | Y | 09__350002022300992.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300992.wav | SPEAKER_01 | agent_candidate | 15 | 23.729 | 0.388548 | 1.581933 | 1.519 | 3.322 | 61.625 | DETECTED | Y | 09__350002022300992.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300993.wav | SPEAKER_00 | customer_candidate | 4 | 14.26 | 0.48041 | 3.565 | 4.219 | 0.925 | 15.742 | DETECTED | Y | 09__350002022300993.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300993.wav | SPEAKER_01 | agent_candidate | 6 | 16.841 | 0.567362 | 2.806833 | 0.9365 | 0.031 | 29.714 | DETECTED | Y | 09__350002022300993.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300994.wav | SPEAKER_00 | customer_candidate | 4 | 8.168 | 0.596814 | 2.042 | 1.0885 | 1.077 | 13.514 | DETECTED | Y | 09__350002022300994.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300994.wav | SPEAKER_01 | agent_candidate | 2 | 6.261 | 0.457475 | 3.1305 | 3.1305 | 8.165 | 14.763 | DETECTED | Y | 09__350002022300994.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300995.wav | SPEAKER_00 | customer_candidate | 45 | 89.491 | 0.585337 | 1.988689 | 1.384 | 1.87 | 153.83 | DETECTED | Y | 09__350002022300995.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300995.wav | SPEAKER_01 | agent_candidate | 34 | 55.691 | 0.36426 | 1.637971 | 0.5315 | 9.278 | 154.758 | DETECTED | Y | 09__350002022300995.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300996.wav | SPEAKER_00 | customer_candidate | 22 | 41.901 | 0.699644 | 1.904591 | 0.8355 | 0.368 | 59.92 | DETECTED | Y | 09__350002022300996.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300996.wav | SPEAKER_01 | agent_candidate | 23 | 23.676 | 0.395331 | 1.029391 | 0.422 | 0.031 | 58.385 | DETECTED | Y | 09__350002022300996.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300997.wav | SPEAKER_00 | customer_candidate | 30 | 30.391 | 0.296891 | 1.013033 | 0.8015 | 0.79 | 103.053 | DETECTED | Y | 09__350002022300997.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300997.wav | SPEAKER_01 | agent_candidate | 29 | 65.105 | 0.636015 | 2.245 | 1.35 | 2.242 | 103.154 | DETECTED | Y | 09__350002022300997.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300998.wav | SPEAKER_00 | customer_candidate | 8 | 12.353 | 0.277914 | 1.544125 | 1.1395 | 0.318 | 43.113 | DETECTED | Y | 09__350002022300998.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300998.wav | SPEAKER_01 | agent_candidate | 6 | 19.035 | 0.428244 | 3.1725 | 1.3585 | 5.954 | 44.767 | DETECTED | Y | 09__350002022300998.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300999.wav | SPEAKER_00 | customer_candidate | 6 | 9.265 | 0.275482 | 1.544167 | 0.616 | 0.773 | 34.405 | DETECTED | Y | 09__350002022300999.wav::SPEAKER_00 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file | ||
| 09__350002022300999.wav | SPEAKER_01 | agent_candidate | 6 | 22.596 | 0.67186 | 3.766 | 3.417 | 1.432 | 32.887 | DETECTED | Y | 09__350002022300999.wav::SPEAKER_01 | ROLE_MAPPING_REQUIRED_BEFORE_CUSTOMER_AGENT_CLAIM | call_speaker_side_record_not_audio_file |
결과 해석
슬롯을 고정하면 누락 통화를 포함한 전체 1,000통화 기준 화자 후보 비교가 가능하다.
한계 및 논문 반영 기준
SPEAKER_00을 고객, SPEAKER_01을 상담사로 자동 치환하지 않는다.
9. 화자별 발화 구조 및 연관성 상세보고서
논문 작성용 상세 본문
현재 9번 분석은 화자 후보별 발화량, 발화 비중, 턴 수, 평균 세그먼트 길이, 응답쌍과 같은 구조적 지표를 다룬다.
S0 발화 이후 S1 반응을 시간 순서 기반 연관성 후보로 분석할 수 있으나, segment-level stress_score가 없어 stress 전이 또는 고스트레스 반응은 아직 계산할 수 없다.
따라서 운영기와 문서에는 READY 구조 지표와 PENDING stress 지표를 동시에 표시한다.
현재 화자별 분석은 발화시간, 발화 비중, 세그먼트 수, 턴 수, 평균 구간 길이, 응답쌍과 같은 구조 지표를 중심으로 수행한다.
이 지표는 통화 내부 상호작용의 형태를 설명하지만 감정 또는 스트레스의 방향을 직접 의미하지 않는다.
segment-level stress_score가 0건이므로 S0 stress와 S1 stress의 평균, 전이, 고스트레스 반응은 모두 PENDING으로 유지한다.
분석 수치 및 결과표
표 9-1. 분석 가능 지표
| 지표 | 상태 | 해석 |
|---|---|---|
| 화자별 발화시간 | READY | 구조 비교 |
| 화자별 발화 비중 | READY | 구조 비교 |
| 턴 수/세그먼트 수 | READY | 상호작용 강도 후보 |
| S0→S1 응답쌍 | READY | 시간 순서 후보 |
표 9-2. 미완료 stress 지표
| 지표 | 상태 | 필요 조건 |
|---|---|---|
| S0 stress 평균 | PENDING | segment STT/WPM/stress |
| S1 stress 평균 | PENDING | segment STT/WPM/stress |
| S0 stress→S1 stress | PENDING | 응답쌍 stress 병합 |
| high-stress segment | PENDING | stress_score 완료 |
표 9-Q. 연구 질문
| 번호 | 연구 질문 |
|---|---|
| 1 | 화자 후보별 발화량·비중·턴·응답 구조는 어떤 패턴을 보이는가? |
| 2 | stress 관련 분석은 현재 어디까지 가능한가? |
표 9-M. 분석 방법 및 도구
| 순서 | 방법/도구 |
|---|---|
| 1 | Speaker descriptive statistics |
| 2 | Turn and speech-ratio analysis |
| 3 | Sequential association candidates |
| 4 | READY/PENDING separation |
분석 그림 및 도식
원본 분석 산출물 연계
산출물 CSV: research_continuity/26_speaker_level_stress_interaction_v201/speaker_interaction_types_v201.csv (행 1,004, 열 4)
대용량 표 요약·앞/뒤 표본 포함, 원본은 데이터 ZIP 수록
| call_id | interaction_type | basis | role_mapping |
|---|---|---|---|
| __SUMMARY__ | Type A: balanced speech ratio | count | 179 |
| __SUMMARY__ | Type B: SPEAKER_00 dominant speech ratio | count | 434 |
| __SUMMARY__ | Type C: SPEAKER_01 dominant speech ratio | count | 347 |
| __SUMMARY__ | Type E: SPEAKER_01 zero-slot | count | 40 |
| 00__350002022300000.wav | Type A: balanced speech ratio | speech_ratio_gap | pending |
| 00__350002022300001.wav | Type A: balanced speech ratio | speech_ratio_gap | pending |
| 00__350002022300002.wav | Type B: SPEAKER_00 dominant speech ratio | speech_ratio_gap | pending |
| 00__350002022300003.wav | Type A: balanced speech ratio | speech_ratio_gap | pending |
| 00__350002022300004.wav | Type B: SPEAKER_00 dominant speech ratio | speech_ratio_gap | pending |
| 00__350002022300005.wav | Type C: SPEAKER_01 dominant speech ratio | speech_ratio_gap | pending |
| 00__350002022300006.wav | Type A: balanced speech ratio | speech_ratio_gap | pending |
| 00__350002022300007.wav | Type A: balanced speech ratio | speech_ratio_gap | pending |
| 00__350002022300008.wav | Type A: balanced speech ratio | speech_ratio_gap | pending |
| 00__350002022300009.wav | Type A: balanced speech ratio | speech_ratio_gap | pending |
| 00__350002022300010.wav | Type E: SPEAKER_01 zero-slot | speech_ratio_gap | pending |
| 00__350002022300011.wav | Type A: balanced speech ratio | speech_ratio_gap | pending |
| 00__350002022300012.wav | Type B: SPEAKER_00 dominant speech ratio | speech_ratio_gap | pending |
| 00__350002022300013.wav | Type B: SPEAKER_00 dominant speech ratio | speech_ratio_gap | pending |
| 00__350002022300014.wav | Type B: SPEAKER_00 dominant speech ratio | speech_ratio_gap | pending |
| 00__350002022300015.wav | Type B: SPEAKER_00 dominant speech ratio | speech_ratio_gap | pending |
| 00__350002022300016.wav | Type C: SPEAKER_01 dominant speech ratio | speech_ratio_gap | pending |
| 00__350002022300017.wav | Type C: SPEAKER_01 dominant speech ratio | speech_ratio_gap | pending |
| 00__350002022300018.wav | Type B: SPEAKER_00 dominant speech ratio | speech_ratio_gap | pending |
| 00__350002022300019.wav | Type B: SPEAKER_00 dominant speech ratio | speech_ratio_gap | pending |
| 00__350002022300020.wav | Type C: SPEAKER_01 dominant speech ratio | speech_ratio_gap | pending |
| ... 중간 954행은 전체 데이터 부록 ZIP에 원본으로 포함 ... | |||
| 09__350002022300975.wav | Type B: SPEAKER_00 dominant speech ratio | speech_ratio_gap | pending |
| 09__350002022300976.wav | Type B: SPEAKER_00 dominant speech ratio | speech_ratio_gap | pending |
| 09__350002022300977.wav | Type B: SPEAKER_00 dominant speech ratio | speech_ratio_gap | pending |
| 09__350002022300978.wav | Type B: SPEAKER_00 dominant speech ratio | speech_ratio_gap | pending |
| 09__350002022300979.wav | Type E: SPEAKER_01 zero-slot | speech_ratio_gap | pending |
| 09__350002022300980.wav | Type C: SPEAKER_01 dominant speech ratio | speech_ratio_gap | pending |
| 09__350002022300981.wav | Type A: balanced speech ratio | speech_ratio_gap | pending |
| 09__350002022300982.wav | Type E: SPEAKER_01 zero-slot | speech_ratio_gap | pending |
| 09__350002022300983.wav | Type B: SPEAKER_00 dominant speech ratio | speech_ratio_gap | pending |
| 09__350002022300984.wav | Type E: SPEAKER_01 zero-slot | speech_ratio_gap | pending |
| 09__350002022300985.wav | Type C: SPEAKER_01 dominant speech ratio | speech_ratio_gap | pending |
| 09__350002022300986.wav | Type A: balanced speech ratio | speech_ratio_gap | pending |
| 09__350002022300987.wav | Type B: SPEAKER_00 dominant speech ratio | speech_ratio_gap | pending |
| 09__350002022300988.wav | Type C: SPEAKER_01 dominant speech ratio | speech_ratio_gap | pending |
| 09__350002022300989.wav | Type A: balanced speech ratio | speech_ratio_gap | pending |
| 09__350002022300990.wav | Type B: SPEAKER_00 dominant speech ratio | speech_ratio_gap | pending |
| 09__350002022300991.wav | Type B: SPEAKER_00 dominant speech ratio | speech_ratio_gap | pending |
| 09__350002022300992.wav | Type A: balanced speech ratio | speech_ratio_gap | pending |
| 09__350002022300993.wav | Type A: balanced speech ratio | speech_ratio_gap | pending |
| 09__350002022300994.wav | Type B: SPEAKER_00 dominant speech ratio | speech_ratio_gap | pending |
| 09__350002022300995.wav | Type B: SPEAKER_00 dominant speech ratio | speech_ratio_gap | pending |
| 09__350002022300996.wav | Type B: SPEAKER_00 dominant speech ratio | speech_ratio_gap | pending |
| 09__350002022300997.wav | Type C: SPEAKER_01 dominant speech ratio | speech_ratio_gap | pending |
| 09__350002022300998.wav | Type C: SPEAKER_01 dominant speech ratio | speech_ratio_gap | pending |
| 09__350002022300999.wav | Type C: SPEAKER_01 dominant speech ratio | speech_ratio_gap | pending |
산출물 CSV: research_continuity/26_speaker_level_stress_interaction_v201/speaker_sequential_association_v201.csv (행 8, 열 6)
전체 행 포함
| pair_metric | correlation | p_value | n | interpretation | claim_level |
|---|---|---|---|---|---|
| S0 total speech sec → S1 total speech sec | 0.341355 | 1.26501e-27 | 960 | call-level speech amount association | SEQUENTIAL_ASSOCIATION_NOT_CAUSAL |
| S0 turn count → S1 turn count | 0.862617 | 8.5856e-286 | 960 | call-level turn count association | SEQUENTIAL_ASSOCIATION_NOT_CAUSAL |
| S0 mean turn duration → S1 mean turn duration | -0.372985 | 4.72794e-33 | 960 | call-level mean turn duration association | SEQUENTIAL_ASSOCIATION_NOT_CAUSAL |
| S0 segment duration → next S1 segment duration | -0.082305 | 1.64032e-43 | 28149 | response-pair sequential association | SEQUENTIAL_ASSOCIATION_NOT_CAUSAL |
| S0 segment duration → S1 response latency | 0.078083 | 2.53106e-39 | 28149 | response latency association | SEQUENTIAL_ASSOCIATION_NOT_CAUSAL |
| S0 stress → S1 stress | 0 | PENDING: segment-level acoustic/STT/stress features are required. Current v201 keeps this as a safe analysis slot, not a claim. | PENDING_FEATURE_MERGE | ||
| S0 energy → S1 response latency | 0 | PENDING: segment-level acoustic/STT/stress features are required. Current v201 keeps this as a safe analysis slot, not a claim. | PENDING_FEATURE_MERGE | ||
| S0 wpm → S1 wpm | 0 | PENDING: segment-level acoustic/STT/stress features are required. Current v201 keeps this as a safe analysis slot, not a claim. | PENDING_FEATURE_MERGE |
결과 해석
현재 결과는 상호작용 구조의 기술통계와 시간 순서 후보 분석으로 활용할 수 있다.
한계 및 논문 반영 기준
stress 관련 결론과 고객/상담사 역할 기반 결론은 보류한다.
10. TIIS 제출용 표·그림 상세보고서
논문 작성용 상세 본문
10번은 TIIS 확장 논문에 사용할 데이터셋 요약, 화자분리 결과, 화자별 기술통계, 순차 연관성, 고스트레스 세그먼트 표와 그림 후보를 정리한다.
현재 Table 1~3의 데이터/구조 항목은 활용할 수 있지만, Table 4~5의 stress 관련 값은 segment-level stress가 완료될 때까지 PENDING으로 유지한다.
문서에는 준비된 표와 미완료 표를 구분해 표시하여 연구 진행 상태를 투명하게 제시한다.
TIIS용 산출물은 데이터셋 요약, 화자분리 결과, 화자 구조 기술통계, 순차 연관성, 고스트레스 세그먼트 표로 구성된다.
현재 데이터셋과 화자분리 구조는 준비됐고 화자별 구조 통계는 부분적으로 준비됐다. stress 연관 표와 고스트레스 표는 미완료이다.
제출용 표·그림에는 READY, PARTIAL, PENDING 상태를 명시해 값이 없는 표를 임의로 채우지 않는다.
분석 수치 및 결과표
표 10-1. TIIS 표 준비 상태
| 표 | 내용 | 상태 |
|---|---|---|
| Table 1 | Dataset summary | READY |
| Table 2 | Diarization/slot summary | READY |
| Table 3 | Speaker descriptive statistics | PARTIAL READY |
| Table 4 | Sequential stress association | PENDING |
| Table 5 | High-stress segments | PENDING |
표 10-2. TIIS 그림 준비 상태
| 그림 | 내용 | 상태 |
|---|---|---|
| Figure 1 | Research pipeline | READY |
| Figure 2 | Speaker segment/slot flow | READY |
| Figure 3 | Speech volume/turn structure | READY |
| Figure 4 | Stress response association | PENDING |
표 10-Q. 연구 질문
| 번호 | 연구 질문 |
|---|---|
| 1 | TIIS 논문에 사용할 표와 그림 중 현재 확정 가능한 것은 무엇인가? |
| 2 | PENDING 결과를 제출 문서에서 어떻게 표시할 것인가? |
표 10-M. 분석 방법 및 도구
| 순서 | 방법/도구 |
|---|---|
| 1 | Submission table mapping |
| 2 | Figure readiness matrix |
| 3 | Claim-level status |
| 4 | Appendix planning |
분석 그림 및 도식
원본 분석 산출물 연계
산출물 CSV: research_continuity/27_tiis_extension_tables_v201/table3_speaker_level_descriptive_statistics_v201.csv (행 2, 열 16)
전체 행 포함
| speaker_slot | call_count | detected_call_count | zero_slot_count | segment_count | total_speech_sec | mean_segment_sec | median_segment_sec | speech_ratio_mean | turn_count | mean_energy | mean_pitch | mean_wpm | mean_stress_score | std_stress_score | claim_policy |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SPEAKER_00 후보 | 1000 | 1000 | 0 | 29386 | 60899.981 | 2.037024 | 1.889537 | 0.465908 | 29386 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SPEAKER_LABEL_CANDIDATE_NOT_CUSTOMER_AGENT_ROLE |
| SPEAKER_01 후보 | 1000 | 960 | 40 | 28233 | 51768.715 | 1.874686 | 1.706773 | 0.418215 | 28233 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SPEAKER_LABEL_CANDIDATE_NOT_CUSTOMER_AGENT_ROLE |
산출물 CSV: research_continuity/27_tiis_extension_tables_v201/table4_sequential_association_results_v201.csv (행 8, 열 6)
전체 행 포함
| pair_metric | correlation | p_value | n | interpretation | claim_level |
|---|---|---|---|---|---|
| S0 total speech sec → S1 total speech sec | 0.341355 | 1.26501e-27 | 960 | call-level speech amount association | SEQUENTIAL_ASSOCIATION_NOT_CAUSAL |
| S0 turn count → S1 turn count | 0.862617 | 8.5856e-286 | 960 | call-level turn count association | SEQUENTIAL_ASSOCIATION_NOT_CAUSAL |
| S0 mean turn duration → S1 mean turn duration | -0.372985 | 4.72794e-33 | 960 | call-level mean turn duration association | SEQUENTIAL_ASSOCIATION_NOT_CAUSAL |
| S0 segment duration → next S1 segment duration | -0.082305 | 1.64032e-43 | 28149 | response-pair sequential association | SEQUENTIAL_ASSOCIATION_NOT_CAUSAL |
| S0 segment duration → S1 response latency | 0.078083 | 2.53106e-39 | 28149 | response latency association | SEQUENTIAL_ASSOCIATION_NOT_CAUSAL |
| S0 stress → S1 stress | 0 | PENDING: segment-level acoustic/STT/stress features are required. Current v201 keeps this as a safe analysis slot, not a claim. | PENDING_FEATURE_MERGE | ||
| S0 energy → S1 response latency | 0 | PENDING: segment-level acoustic/STT/stress features are required. Current v201 keeps this as a safe analysis slot, not a claim. | PENDING_FEATURE_MERGE | ||
| S0 wpm → S1 wpm | 0 | PENDING: segment-level acoustic/STT/stress features are required. Current v201 keeps this as a safe analysis slot, not a claim. | PENDING_FEATURE_MERGE |
산출물 CSV: research_continuity/27_tiis_extension_tables_v201/table5_high_stress_segment_analysis_v201.csv (행 1, 열 7)
전체 행 포함
| analysis | status | threshold | segment_count | high_stress_segment_count | high_stress_ratio | note |
|---|---|---|---|---|---|---|
| high_stress_segment_detection | PENDING_SEGMENT_LEVEL_STRESS_SCORE | 57619 | speaker_actual_segments_v181.csv에 segment-level stress_score가 없어 고스트레스 세그먼트 확정은 보류합니다. 9번 화면은 분석 슬롯만 안전하게 확보합니다. |
산출물 CSV: research_continuity/27_tiis_extension_tables_v201/table6_paper_use_policy_v201.csv (행 6, 열 3)
전체 행 포함
| item | judgment | note |
|---|---|---|
| 본문 대체 | FORBIDDEN | 0413 n=998 통화 전체 기반 본문 분석 유지 |
| 부록/확장 분석 | ALLOWED | speaker-level exploratory analysis로 사용 가능 |
| 고객/상담사 확정 | FORBIDDEN_UNTIL_ROLE_MAPPING | SPEAKER_00/01은 후보 라벨 |
| 인과 주장 | FORBIDDEN | S0→S1은 시간 순서 기반 연관성 후보 |
| sequential association | ALLOWED | response-pair association으로 표현 |
| stress imbalance | CONDITIONAL | segment-level stress_score가 있을 때만 확정 계산 |
산출물 CSV: results/tables/applied_sciences_required_sections.csv (행 20, 열 3)
전체 행 포함
| section_or_statement | source_or_field | status |
|---|---|---|
| Title | title_en/title_ko | Required |
| Author list | authors | Required |
| Affiliations | authors.affiliation | Required |
| Abstract | 01_abstract | Required |
| Keywords | keywords | Required |
| Introduction | 02_introduction | Required |
| Materials and Methods | 04_data_preprocessing + 05_stress_index_method + 06_stt_reliability | Required |
| Results | 07_construct_validity + 08_robustness_sensitivity | Required |
| Discussion | 09_discussion | Required |
| Conclusions | 10_limitations_conclusion | Required |
| Supplementary Materials | declarations | If applicable |
| Author Contributions | author_contributions | Required |
| Funding | funding | Required |
| Institutional Review Board Statement | irb_statement | Required when human data are used |
| Informed Consent Statement | informed_consent_statement | Required when human data are used |
| Data Availability Statement | data_availability_statement | Required |
| Acknowledgments | acknowledgments | If applicable |
| Conflicts of Interest | conflicts_of_interest | Required |
| References | references | Required |
| Generative AI disclosure | genai_disclosure | If applicable / journal-policy dependent |
산출물 CSV: results/tables/coach/today_task_plan.csv (행 4, 열 6)
전체 행 포함
| priority | task | reason | menu | command_hint | blocking_submission |
|---|---|---|---|---|---|
| 6 | 데이터 품질 FAIL 해소 | 품질 FAIL=5 | 08 데이터 품질 점검 / 09 자동 조치 | python src/data_quality.py && python src/quality_actions.py --smart-fix | YES |
| 7 | 제출 게이트 재점검 | 제출 게이트 FAIL=3 | 33 제출 게이트 | python src/ops_manager.py --submission-gate --profile n1000 | YES |
| 9 | AS-IS/TO-BE 및 3자 비교 재실행 | 최종 수치 기준을 문서·AS-IS·TO-BE로 재확인 | 57, 64 | python src/repro_manager.py --three-way --profile n1000 | NO |
| 10 | 교수님 검토팩/미팅자료 생성 | 교수님 확인사항을 정리 | 34, 73 | python src/paper_coach.py --meeting --profile n1000 | NO |
산출물 CSV: results/tables/complete_report_checklist.csv (행 34, 열 10)
전체 행 포함
| order | group | title | kind | required | exists | rows_or_note | path | status | description |
|---|---|---|---|---|---|---|---|---|---|
| 1 | summary | 요약 카드 | summary | True | True | 7 | summary cards | 완성 | dataset, clean_n, mean/std stress_score, generated_at |
| 2 | table | descriptive_stats | csv | True | True | 5 | C:\AI\sci_voc_bot\results\tables\descriptive_statistics.csv | 완성 | |
| 3 | table | correlation_vars | csv | True | True | 4 | C:\AI\sci_voc_bot\results\tables\correlation_validity.csv | 완성 | |
| 4 | table | correlation_matrix | csv | True | True | 7 | C:\AI\sci_voc_bot\results\tables\correlation_matrix.csv | 완성 | |
| 5 | table | validity_summary | csv | True | True | 1 | C:\AI\sci_voc_bot\results\tables\validity_structure_summary.csv | 완성 | |
| 6 | table | discriminant_details | csv | True | True | 4 | C:\AI\sci_voc_bot\results\tables\discriminant_details.csv | 완성 | |
| 7 | table | bootstrap_ci | csv | True | True | 4 | C:\AI\sci_voc_bot\results\tables\bootstrap_ci.csv | 완성 | |
| 8 | table | univariate_ols | csv | True | True | 4 | C:\AI\sci_voc_bot\results\tables\univariate_ols.csv | 완성 | |
| 9 | table | multivariate_ols | csv | True | True | 4 | C:\AI\sci_voc_bot\results\tables\multivariate_ols.csv | 완성 | |
| 10 | table | vif | csv | True | True | 4 | C:\AI\sci_voc_bot\results\tables\vif.csv | 완성 | |
| 11 | table | sensitivity | csv | True | True | 6 | C:\AI\sci_voc_bot\results\tables\weight_sensitivity.csv | 완성 | |
| 12 | table | leave_one_out | csv | True | True | 4 | C:\AI\sci_voc_bot\results\tables\loo_stability.csv | 완성 | |
| 13 | table | anova | csv | True | True | 4 | C:\AI\sci_voc_bot\results\tables\anova_profile.csv | 완성 | |
| 14 | table | tukey | csv | True | True | 12 | C:\AI\sci_voc_bot\results\tables\tukey_hsd.csv | 완성 | |
| 15 | table | stt_reliability | csv | False | True | 1 | C:\AI\sci_voc_bot\results\tables\stt_reliability.csv | 완성 | Required for SCI final, but unavailable when --skip-stt is used. |
| 16 | table | stt_error_sensitivity | csv | False | True | 6 | C:\AI\sci_voc_bot\results\tables\stt_error_sensitivity.csv | 완성 | |
| 17 | table | outlier_robustness | csv | False | True | 4 | C:\AI\sci_voc_bot\results\tables\outlier_robustness.csv | 완성 | |
| 18 | figure | stress_score 분포 | png | True | True | C:\AI\sci_voc_bot\results\figures\fig02_stress_distribution.png | 완성 | ||
| 19 | figure | scatter: stress_score vs energy_mean | png | True | True | C:\AI\sci_voc_bot\results\figures\scatter_energy_mean.png | 완성 | ||
| 20 | figure | scatter: stress_score vs pitch_mean | png | True | True | C:\AI\sci_voc_bot\results\figures\scatter_pitch_mean.png | 완성 | ||
| 21 | figure | scatter: stress_score vs wpm | png | True | True | C:\AI\sci_voc_bot\results\figures\scatter_wpm.png | 완성 | ||
| 22 | figure | scatter: stress_score vs duration_sec | png | True | True | C:\AI\sci_voc_bot\results\figures\scatter_duration_sec.png | 완성 | ||
| 23 | figure | Bootstrap 95% CI | png | True | True | C:\AI\sci_voc_bot\results\figures\fig04_bootstrap_ci.png | 완성 | ||
| 24 | figure | 민감도 분석 | png | True | True | C:\AI\sci_voc_bot\results\figures\fig05_weight_sensitivity.png | 완성 | ||
| 25 | figure | ANOVA: energy_mean | png | True | True | C:\AI\sci_voc_bot\results\figures\anova_energy_mean.png | 완성 | ||
| 26 | figure | ANOVA: pitch_mean | png | True | True | C:\AI\sci_voc_bot\results\figures\anova_pitch_mean.png | 완성 | ||
| 27 | figure | ANOVA: wpm | png | True | True | C:\AI\sci_voc_bot\results\figures\anova_wpm.png | 완성 | ||
| 28 | figure | ANOVA: duration_sec | png | True | True | C:\AI\sci_voc_bot\results\figures\anova_duration_sec.png | 완성 | ||
| 29 | figure | Tukey: energy_mean | png | False | True | C:\AI\sci_voc_bot\results\figures\tukey_energy_mean.png | 완성 | ||
| 30 | figure | Tukey: pitch_mean | png | False | True | C:\AI\sci_voc_bot\results\figures\tukey_pitch_mean.png | 완성 | ||
| 31 | figure | Tukey: wpm | png | False | True | C:\AI\sci_voc_bot\results\figures\tukey_wpm.png | 완성 | ||
| 32 | figure | Tukey: duration_sec | png | False | True | C:\AI\sci_voc_bot\results\figures\tukey_duration_sec.png | 완성 | ||
| 33 | figure | STT 오류 민감도 | png | False | True | C:\AI\sci_voc_bot\results\figures\fig_stt_error_sensitivity.png | 완성 | ||
| 34 | figure | STT Reliability | png | False | True | C:\AI\sci_voc_bot\results\figures\fig_stt_reliability.png | 완성 |
산출물 CSV: results/tables/descriptive_statistics.csv (행 5, 열 9)
전체 행 포함
| variable | count | mean | std | min | 25% | 50% | 75% | max |
|---|---|---|---|---|---|---|---|---|
| stress_score | 1000.0 | 0.6364881461037636 | 0.10426262134100811 | 0.0 | 0.5771021479295023 | 0.6362956576335654 | 0.6973088546912881 | 1.0 |
| energy_mean | 1000.0 | 0.048815202135126995 | 0.022995187709716303 | 0.0072453501634299 | 0.03253942169249055 | 0.04580023698508735 | 0.059025459922850076 | 0.1768262088298797 |
| pitch_mean | 1000.0 | 236.38900857253438 | 29.303475881585253 | 131.448083315726 | 218.04254036934034 | 234.67691727947954 | 251.77567676297855 | 450.8633852164357 |
| wpm | 1000.0 | 83.59627317584393 | 20.067911629281365 | 0.0 | 71.87679211853018 | 86.06739012067416 | 97.81761761168447 | 133.33333333333331 |
| duration_sec | 1000.0 | 136.10352 | 153.16339535570006 | 2.1 | 48.7275 | 89.61 | 165.255 | 1421.46 |
산출물 CSV: results/tables/discriminant_details.csv (행 4, 열 4)
전체 행 포함
| variable | r | p_value | abs_r |
|---|---|---|---|
| wpm | |||
| duration_sec | -0.5591567121462838 | 1.4795511118265382e-09 | 0.5591567121462838 |
| word_cnt | |||
| text_len |
산출물 CSV: results/tables/feature_descriptive_statistics.csv (행 4, 열 9)
전체 행 포함
| variable | count | mean | std | min | 25% | 50% | 75% | max |
|---|---|---|---|---|---|---|---|---|
| energy_mean | 1000.0 | 0.04881520213512704 | 0.022995187709716303 | 0.0072453501634299755 | 0.03253942169249058 | 0.045800236985087395 | 0.05902545992285013 | 0.17682620882987976 |
| pitch_mean | 1000.0 | 236.38900857253438 | 29.303475881585253 | 131.448083315726 | 218.04254036934034 | 234.67691727947954 | 251.77567676297858 | 450.86338521643574 |
| wpm | 1000.0 | 83.59627317584393 | 20.06791162928137 | 0.0 | 71.87679211853018 | 86.06739012067416 | 97.81761761168447 | 133.33333333333331 |
| duration_sec | 1000.0 | 136.10352 | 153.16339535570006 | 2.1 | 48.7275 | 89.61 | 165.255 | 1421.46 |
산출물 CSV: results/tables/finalization/journal_recommendations.csv (행 8, 열 10)
전체 행 포함
| rank_preliminary | journal | publisher | candidate_level | fit_area | suitability_score_rule_based | why_suitable | main_risk | required_before_submission | recommended_positioning |
|---|---|---|---|---|---|---|---|---|---|
| 1 | Applied Sciences | MDPI | SCI/SCIE급 후보 - 최종 색인 상태 수동 확인 필요 | applied signal processing, speech processing, AI application | 60 | 음성처리, 응용 AI, 검증 프레임워크 관점으로 맞출 수 있음 | 범위가 넓어 방법론/응용 기여를 명확히 써야 함 | 현재 stress_score n이 부족하면 투고 전 보완 필요; 응용 시스템/검증 프레임워크형 원고로 포지셔닝 가능; 저널 홈페이지/Clarivate/Scopus 등재 상태 최신 확인; 최근 게재 논문 주제와 투고 규정 확인 | speech-based stress-related index + construct validity + operational VOC analytics |
| 2 | Electronics | MDPI | SCI/SCIE급 후보 - 최종 색인 상태 수동 확인 필요 | AI system, speech processing, digital service platform | 60 | 콜센터 AI 분석 시스템과 자동화 플랫폼 관점으로 맞출 수 있음 | 논문 기여가 단순 구현으로 보이지 않게 검증 결과를 강화해야 함 | 현재 stress_score n이 부족하면 투고 전 보완 필요; 응용 시스템/검증 프레임워크형 원고로 포지셔닝 가능; 저널 홈페이지/Clarivate/Scopus 등재 상태 최신 확인; 최근 게재 논문 주제와 투고 규정 확인 | speech-based stress-related index + construct validity + operational VOC analytics |
| 3 | Information | MDPI | SCI/SCIE급 후보 - 최종 색인 상태 수동 확인 필요 | information systems, text/speech analytics, decision support | 60 | VOC 분석과 품질관리 의사결정 지원 도구 관점이 적합 가능 | 음성처리보다는 정보시스템·분석 프레임워크 기여를 강조해야 함 | 현재 stress_score n이 부족하면 투고 전 보완 필요; 응용 시스템/검증 프레임워크형 원고로 포지셔닝 가능; 저널 홈페이지/Clarivate/Scopus 등재 상태 최신 확인; 최근 게재 논문 주제와 투고 규정 확인 | speech-based stress-related index + construct validity + operational VOC analytics |
| 4 | IEEE Access | IEEE | SCI/SCIE급 후보 - 최종 색인 상태 수동 확인 필요 | engineering application, speech/AI, scalable system validation | 55 | 실제 VOC 데이터 기반 시스템형 연구와 맞출 수 있음 | 실험 완성도와 비교실험, 재현성 설명이 약하면 리스크가 큼 | 현재 stress_score n이 부족하면 투고 전 보완 필요; 저널 홈페이지/Clarivate/Scopus 등재 상태 최신 확인; 최근 게재 논문 주제와 투고 규정 확인 | speech-based stress-related index + construct validity + operational VOC analytics |
| 5 | Sensors | MDPI | SCI/SCIE급 후보 - 최종 색인 상태 수동 확인 필요 | signal sensing, acoustic features, measurement index | 55 | 음성 신호를 센싱 데이터로 해석하면 적합 가능 | 센서/측정 관점의 기여를 분명히 해야 함 | 현재 stress_score n이 부족하면 투고 전 보완 필요; 저널 홈페이지/Clarivate/Scopus 등재 상태 최신 확인; 최근 게재 논문 주제와 투고 규정 확인 | speech-based stress-related index + construct validity + operational VOC analytics |
| 6 | Applied Acoustics | Elsevier | SCI/SCIE급 후보 - 최종 색인 상태 수동 확인 필요 | acoustic analysis, speech acoustics, applied acoustic measurement | 49 | pitch/energy/duration 중심의 음향 분석 기여와 연결 가능 | 콜센터 운영/경영 관점보다 음향 분석 깊이를 더 요구할 수 있음 | 현재 stress_score n이 부족하면 투고 전 보완 필요; 음성처리 전문성 보강과 추가 비교실험 필요; 저널 홈페이지/Clarivate/Scopus 등재 상태 최신 확인; 최근 게재 논문 주제와 투고 규정 확인 | speech-based stress-related index + construct validity + operational VOC analytics |
| 7 | Speech Communication | Elsevier | SCI/SCIE급 후보 - 최종 색인 상태 수동 확인 필요 | speech science, speech technology, spoken interaction | 49 | 자연 발화/통화 음성 기반 stress-related speech index로 연결 가능 | 언어·음성학적 해석과 기존 speech literature 보강 필요 | 현재 stress_score n이 부족하면 투고 전 보완 필요; 음성처리 전문성 보강과 추가 비교실험 필요; 저널 홈페이지/Clarivate/Scopus 등재 상태 최신 확인; 최근 게재 논문 주제와 투고 규정 확인 | speech-based stress-related index + construct validity + operational VOC analytics |
| 8 | Computer Speech & Language | Elsevier | SCI/SCIE급 후보 - 최종 색인 상태 수동 확인 필요 | computational speech processing, ASR, speech analytics | 49 | STT/WPM과 음성 특징 분석의 계산적 접근에 맞출 수 있음 | 모델 비교, 알고리즘 기여, 데이터 공개성 요구가 높을 수 있음 | 현재 stress_score n이 부족하면 투고 전 보완 필요; 음성처리 전문성 보강과 추가 비교실험 필요; 저널 홈페이지/Clarivate/Scopus 등재 상태 최신 확인; 최근 게재 논문 주제와 투고 규정 확인 | speech-based stress-related index + construct validity + operational VOC analytics |
산출물 CSV: results/tables/future_analysis/stt_wpm_reliability_sources.csv (행 3, 열 3)
전체 행 포함
| source | path | status |
|---|---|---|
| stt_reliability_summary | C:\AI\sci_voc_bot\results\tables\stt_reliability.csv | available |
| stt_error_sensitivity | C:\AI\sci_voc_bot\results\tables\stt_error_sensitivity.csv | available |
| kspon_by_file | C:\AI\sci_voc_bot\results\stt_validation\kspon_wer_cer_by_file.csv | available |
산출물 텍스트: research_continuity/27_tiis_extension_tables_v201/tiis_extension_tables_v201.md
# TIIS 확장판 논문용 표/그림 v201 ## Table 1. Dataset summary | dataset_component | n_or_count | role | interpretation_policy | | --- | --- | --- | --- | | AS-IS/0413 main body | 998 | main paper baseline | 본문 기준 유지 | | TO-BE call audio | 1000 | reproducibility / extension source | 본문 대체 금지 | | pyannote SPEAKER segments | 57619 | speaker-level extension | role mapping 전 후보 라벨 | | call-speaker slots | 2000 | SPEAKER_00 + SPEAKER_01 slot records | 고객/상담사 확정 금지 | | S0→S1 response pairs | 28149 | sequential association | 인과 주장 금지 | ## Table 2. TO-BE / speaker-level n2000 summary | metric | value | note | | --- | --- | --- | | TO-BE calls | 1000 | 통화 기준 | | SPEAKER segments | 57619 | pyannote 산출 세그먼트 | | SPEAKER_00 slots | 1000 | 후보 슬롯 | | SPEAKER_01 slots | 1000 | 후보 슬롯 | | 2,000 slot readiness | True | 1,000 x 2 | | zero-slot | 40 | 미검출 화자 슬롯 보존 | | segment-level stress merge | PENDING_SEGMENT_LEVEL_FEATURE_STRESS_INPUT | 11번 병합 상태 | | segment stress ready | False | False면 Table 4/5 stress 결과는 보류 | ## Table 3. Speaker-level descriptive statistics | speaker_slot | call_count | detected_call_count | zero_slot_count | segment_count | total_speech_sec | mean_segment_sec | median_segment_sec | speech_ratio_mean | turn_count | mean_energy | mean_pitch | mean_wpm | mean_stress_score | std_stress_score | claim_policy | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | SPEAKER_00 후보 | 1000 | 1000 | 0 | 29386 | 60899.981 | 2.037024 | 1.889537 | 0.465908 | 29386 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SPEAKER_LABEL_CANDIDATE_NOT_CUSTOMER_AGENT_ROLE | | SPEAKER_01 후보 | 1000 | 960 | 40 | 28233 | 51768.715 | 1.874686 | 1.706773 | 0.418215 | 28233 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SPEAKER_LABEL_CANDIDATE_NOT_CUSTOMER_AGENT_ROLE | ## Table 4. Sequential association results | pair_metric | correlation | p_value | n | interpretation | claim_level | | --- | --- | --- | --- | --- | --- | | S0 total speech sec → S1 total speech sec | 0.341355 | 1.26501e-27 | 960 | call-level speech amount association | SEQUENTIAL_ASSOCIATION_NOT_CAUSAL | | S0 turn count → S1 turn count | 0.862617 | 8.5856e-286 | 960 | call-level turn count association | SEQUENTIAL_ASSOCIATION_NOT_CAUSAL | | S0 mean turn duration → S1 mean turn duration | -0.372985 | 4.72794e-33 | 960 | call-level mean turn duration association | SEQUENTIAL_ASSOCIATION_NOT_CAUSAL | | S0 segment duration → next S1 segment duration | -0.082305 | 1.64032e-43 | 28149 | response-pair sequential association | SEQUENTIAL_ASSOCIATION_NOT_CAUSAL | | S0 segment duration → S1 response latency | 0.078083 | 2.53106e-39 | 28149 | response latency association | SEQUENTIAL_ASSOCIATION_NOT_CAUSAL | | S0 stress → S1 stress | | | 0 | PENDING: segment-level acoustic/STT/stress features are required. Current v201 keeps this as a safe analysis slot, not a claim. | PENDING_FEATURE_MERGE | | S0 energy → S1 response latency | | | 0 | PENDING: segment-level acoustic/STT/stress features are required. Current v201 keeps this as a safe analysis slot, not a claim. | PENDING_FEATURE_MERGE | | S0 wpm → S1 wpm | | | 0 | PENDING: segment-level acoustic/STT/stress features are required. Current v201 keeps this as a safe analysis slot, not a claim. | PENDING_FEATURE_MERGE | ## Table 5. High-stress segment analysis | analysis | status | threshold | segment_count | high_stress_segment_count | high_stress_ratio | note | | --- | --- | --- | --- | --- | --- | --- | | high_stress_segment_detection | PENDING_SEGMENT_LEVEL_STRESS_SCORE | | 57619 | | | speaker_actual_segments_v181.csv에 segment-level stress_score가 없어 고스트레스 세그먼트 확정은 보류합니다. 9번 화면은 분석 슬롯만 안전하게 확보합니다. | ## Table 6. Paper-use policy | item | judgment | note | | --- | --- | --- | | 본문 대체 | FORBIDDEN | 0413 n=998 통화 전체 기반 본문 분석 유지 | | 부록/확장 분석 | ALLOWED | speaker-level exploratory analysis로 사용 가능 | | 고객/상담사 확정 | FORBIDDEN_UNTIL_ROLE_MAPPING | SPEAKER_00/01은 후보 라벨 | | 인과 주장 | FORBIDDEN | S0→S1은 시간 순서 기반 연관성 후보 | | sequential association | ALLOWED | response-pair association으로 표현 | | stress imbalance | CONDITIONAL | segment-level stress_score가 있을 때만 확정 계산 |
결과 해석
현재 제출 가능한 영역은 데이터셋과 화자분리 구조이며, stress 연관 표는 다음 단계 완료 후 추가해야 한다.
한계 및 논문 반영 기준
PENDING 표를 값이 있는 것처럼 채우지 않는다.
11. Segment-level Feature/Stress Merge 상세보고서
논문 작성용 상세 본문
11번은 7번에서 생성한 57,619개 SPEAKER 세그먼트에 segment_id를 부여하고 원 음성에서 세그먼트 단위 duration, energy, pitch를 산출해 병합 입력 CSV를 만드는 단계이다.
v205 실행 결과 generated_input_count와 matched_segment_count가 모두 57,619건으로 확인되어 이전의 병합 매칭 0건 문제는 해결됐다.
energy는 57,611건, pitch는 53,098건 산출됐다. pitch는 무성음·초단구간에서 값이 존재하지 않을 수 있으므로 전체 세그먼트와 동일하지 않아도 정상이다.
segment-level STT 텍스트가 아직 연결되지 않아 word_count, WPM, stress_score는 모두 0건이며 57,619건 전체가 PENDING stress로 남아 있다.
11번은 화자분리 세그먼트를 실제 음성 구간과 다시 연결하여 duration, energy, pitch를 산출하고 segment_id 기준으로 병합한 단계이다.
생성 입력과 병합 매칭은 각각 57,619건으로 전수 완료되었다. energy는 57,611건, pitch는 53,098건이며 초단구간·무성음에서는 값이 없을 수 있다.
segment-level STT 텍스트가 연결되지 않아 word_count, WPM, stress_score는 0건이다. 따라서 현재 상태는 acoustic feature merge 완료, stress merge PENDING이다.
다음 단계는 세그먼트 STT 병합, WPM 계산, 전역 기준 stress_score 생성, 응답쌍 stress 연결, high-stress 기준 산출 순서이다.
분석 수치 및 결과표
표 11-1. 세그먼트 병합 입력 생성 결과
| 항목 | 값 | 상태 |
|---|---|---|
| segment_count | 57,619 | 완료 |
| generated_input_count | 57,619 | 완료 |
| matched_segment_count | 57,619 | 완료 |
| energy_complete_count | 57,611 | 완료 |
| pitch_complete_count | 53,098 | voiced 기준 완료 |
| wpm_complete_count | 0 | PENDING_STT |
| stress_complete_count | 0 | PENDING |
| pending_stress_count | 57,619 | 다음 단계 |
표 11-2. 0건에서 57,619건으로 변경된 이유
| 구분 | 이전 | 현재 |
|---|---|---|
| 세그먼트 skeleton | 57,619 | 57,619 |
| feature 입력 CSV | 없음 | 생성 완료 |
| segment_id 매칭 | 0 | 57,619 |
| stress_score | 0 | 0 - STT/WPM 미완료 |
표 11-3. 다음 단계
| 순서 | 작업 | 결과 |
|---|---|---|
| 1 | segment별 STT 텍스트 연결 | word_count 생성 |
| 2 | duration 기준 WPM 계산 | wpm_complete 증가 |
| 3 | 전역 기준 stress_score 산출 | stress_complete 증가 |
| 4 | 응답쌍 stress 병합 | S0→S1 탐색 가능 |
| 5 | high-stress 기준 확정 | Table 4/5 확정 |
표 11-Q. 연구 질문
| 번호 | 연구 질문 |
|---|---|
| 1 | 57,619개 세그먼트와 acoustic feature 입력을 실제로 전수 매칭했는가? |
| 2 | stress_score가 0건인 이유와 다음 병합 단계는 무엇인가? |
표 11-M. 분석 방법 및 도구
| 순서 | 방법/도구 |
|---|---|
| 1 | segment_id generation |
| 2 | Audio slicing |
| 3 | RMS energy |
| 4 | pYIN pitch |
| 5 | Segment merge |
| 6 | STT/WPM pending guard |
분석 그림 및 도식
원본 분석 산출물 연계
산출물 JSON: reports/research_console/segment_feature_stress_merge_status_v203.json
전체 행 포함
| 경로 | 값 |
|---|---|
| version | v205_segment_feature_stress_input_builder |
| generated_at | 2026-07-11 08:36:00 |
| menu_no | 11 |
| menu_label | Segment-level Feature/Stress Input Builder |
| state | DONE_SEGMENT_FEATURE_INPUT__STRESS_PENDING_STT_WPM |
| call_count | 1000 |
| segment_count | 57619 |
| speaker_side_record_count | 2000 |
| generated_input_count | 57619 |
| matched_segment_count | 57619 |
| energy_complete_count | 57611 |
| pitch_complete_count | 53098 |
| wpm_complete_count | 0 |
| stress_complete_count | 0 |
| pending_stress_count | 57619 |
| with_pitch | True |
| allow_acoustic_only_stress | False |
| judgment | segment-level acoustic feature 입력 데이터는 생성되었습니다. 다만 segment-level STT 텍스트가 없어 WPM/stress_score는 PENDING입니다. 다음 단계는 세그먼트 STT 병합입니다. |
| main_paper_replacement | FORBIDDEN |
| customer_agent_claim | FORBIDDEN_UNTIL_ROLE_MAPPING_VALIDATED |
| professor_sentence | 11번은 7번에서 생성한 SPEAKER 세그먼트 57,619건을 기준으로 segment_id를 부여하고, 원 음성에서 세그먼트 단위 duration/energy/pitch(선택)를 산출하여 9번/10번 stress 분석을 위한 입력 데이터를 생성하는 단계입니다. STT/WPM이 없는 경우 stress_score는 PENDING으로 유지합니다. |
| meta.audio_file_count | 1000 |
| meta.decode_ok_file_count | 1000 |
| meta.decode_fail_file_count | 0 |
| meta.energy_complete_count | 57611 |
| meta.pitch_complete_count | 53098 |
| meta.feature_complete_count | 0 |
| meta.acoustic_partial_complete_count | 57611 |
| meta.stress_complete_count | 0 |
| outputs.status_json_v205 | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\segment_feature_stress_builder_status_v205.json |
| outputs.merge_status_json_v203_alias | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\segment_feature_stress_merge_status_v203.json |
| outputs.segment_skeleton_csv | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\segment_level_merge_skeleton_v203.csv |
| outputs.segment_level_features_input_v205_csv | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\segment_level_features_input_v205.csv |
| outputs.segment_level_features_input_v203_alias_csv | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\segment_level_features_input_v203.csv |
| outputs.merged_csv_v205 | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\segment_level_features_stress_merged_v205.csv |
| outputs.merged_csv_v203_alias | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\segment_level_features_stress_merged_v203.csv |
| outputs.high_stress_ready_csv | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\high_stress_segment_ready_v203.csv |
| outputs.pair_stress_ready_csv | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\s0_s1_pair_stress_ready_v203.csv |
| outputs.mobile_report_html_v205 | C:\AI\01.sci_voc_bot\reports\research_console\segment_level_feature_stress_merge_v205.html |
| outputs.mobile_report_html_v203_alias | C:\AI\01.sci_voc_bot\reports\research_console\segment_level_feature_stress_merge_v203.html |
| mobile_build_exit_code | 0 |
| mobile_build_tail | [SOC-DEPLOY] package built: C:\AI\01.sci_voc_bot\deploy_packages\sci_voc_soc_mobile_n1000_20260711_083600.zip [SOC-DEPLOY] size=12776717 sha256=726440975683870d... paramiko ready [SOC-DEPLOY] auth=password-auth, user=hdyoo, password=y******!, host=1.234.75.139:22 [SOC-DEPLOY] verifying deployed package [SOC-DEPLOY] local summary rows: 1000 1000 1000 READY [SOC-DEPLOY] http summary rows: 1000 1000 1000 READY DEPLOY_OK |
산출물 JSON: reports/research_console/segment_feature_stress_merge_status_v205.json
전체 행 포함
| 경로 | 값 |
|---|---|
| version | v205_segment_feature_stress_input_builder |
| generated_at | 2026-07-11 08:36:00 |
| menu_no | 11 |
| menu_label | Segment-level Feature/Stress Input Builder |
| state | DONE_SEGMENT_FEATURE_INPUT__STRESS_PENDING_STT_WPM |
| call_count | 1000 |
| segment_count | 57619 |
| speaker_side_record_count | 2000 |
| generated_input_count | 57619 |
| matched_segment_count | 57619 |
| energy_complete_count | 57611 |
| pitch_complete_count | 53098 |
| wpm_complete_count | 0 |
| stress_complete_count | 0 |
| pending_stress_count | 57619 |
| with_pitch | True |
| allow_acoustic_only_stress | False |
| judgment | segment-level acoustic feature 입력 데이터는 생성되었습니다. 다만 segment-level STT 텍스트가 없어 WPM/stress_score는 PENDING입니다. 다음 단계는 세그먼트 STT 병합입니다. |
| main_paper_replacement | FORBIDDEN |
| customer_agent_claim | FORBIDDEN_UNTIL_ROLE_MAPPING_VALIDATED |
| professor_sentence | 11번은 7번에서 생성한 SPEAKER 세그먼트 57,619건을 기준으로 segment_id를 부여하고, 원 음성에서 세그먼트 단위 duration/energy/pitch(선택)를 산출하여 9번/10번 stress 분석을 위한 입력 데이터를 생성하는 단계입니다. STT/WPM이 없는 경우 stress_score는 PENDING으로 유지합니다. |
| meta.audio_file_count | 1000 |
| meta.decode_ok_file_count | 1000 |
| meta.decode_fail_file_count | 0 |
| meta.energy_complete_count | 57611 |
| meta.pitch_complete_count | 53098 |
| meta.feature_complete_count | 0 |
| meta.acoustic_partial_complete_count | 57611 |
| meta.stress_complete_count | 0 |
| outputs.status_json_v205 | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\segment_feature_stress_builder_status_v205.json |
| outputs.merge_status_json_v203_alias | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\segment_feature_stress_merge_status_v203.json |
| outputs.segment_skeleton_csv | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\segment_level_merge_skeleton_v203.csv |
| outputs.segment_level_features_input_v205_csv | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\segment_level_features_input_v205.csv |
| outputs.segment_level_features_input_v203_alias_csv | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\segment_level_features_input_v203.csv |
| outputs.merged_csv_v205 | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\segment_level_features_stress_merged_v205.csv |
| outputs.merged_csv_v203_alias | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\segment_level_features_stress_merged_v203.csv |
| outputs.high_stress_ready_csv | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\high_stress_segment_ready_v203.csv |
| outputs.pair_stress_ready_csv | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\s0_s1_pair_stress_ready_v203.csv |
| outputs.mobile_report_html_v205 | C:\AI\01.sci_voc_bot\reports\research_console\segment_level_feature_stress_merge_v205.html |
| outputs.mobile_report_html_v203_alias | C:\AI\01.sci_voc_bot\reports\research_console\segment_level_feature_stress_merge_v203.html |
산출물 CSV: research_continuity/27_segment_feature_stress_merge_v203/high_stress_segment_ready_v203.csv (행 1, 열 7)
전체 행 포함
| analysis | status | threshold | segment_count | high_stress_segment_count | high_stress_ratio | note |
|---|---|---|---|---|---|---|
| high_stress_segment_detection | PENDING_SEGMENT_LEVEL_STRESS_SCORE | 57619 | segment-level stress_score 산출 전이므로 고스트레스 세그먼트 확정 보류 |
산출물 CSV: research_continuity/27_segment_feature_stress_merge_v203/s0_s1_pair_stress_ready_v203.csv (행 1, 열 6)
전체 행 포함
| pair_metric | status | correlation | p_value | n | note |
|---|---|---|---|---|---|
| S0 stress → S1 stress | PENDING_SEGMENT_LEVEL_STRESS_SCORE | 0 | segment-level stress_score와 S0→S1 pair join 전까지 보류 |
산출물 JSON: research_continuity/27_segment_feature_stress_merge_v203/segment_feature_stress_builder_status_v205.json
전체 행 포함
| 경로 | 값 |
|---|---|
| version | v205_segment_feature_stress_input_builder |
| generated_at | 2026-07-11 08:36:00 |
| menu_no | 11 |
| menu_label | Segment-level Feature/Stress Input Builder |
| state | DONE_SEGMENT_FEATURE_INPUT__STRESS_PENDING_STT_WPM |
| call_count | 1000 |
| segment_count | 57619 |
| speaker_side_record_count | 2000 |
| generated_input_count | 57619 |
| matched_segment_count | 57619 |
| energy_complete_count | 57611 |
| pitch_complete_count | 53098 |
| wpm_complete_count | 0 |
| stress_complete_count | 0 |
| pending_stress_count | 57619 |
| with_pitch | True |
| allow_acoustic_only_stress | False |
| judgment | segment-level acoustic feature 입력 데이터는 생성되었습니다. 다만 segment-level STT 텍스트가 없어 WPM/stress_score는 PENDING입니다. 다음 단계는 세그먼트 STT 병합입니다. |
| main_paper_replacement | FORBIDDEN |
| customer_agent_claim | FORBIDDEN_UNTIL_ROLE_MAPPING_VALIDATED |
| professor_sentence | 11번은 7번에서 생성한 SPEAKER 세그먼트 57,619건을 기준으로 segment_id를 부여하고, 원 음성에서 세그먼트 단위 duration/energy/pitch(선택)를 산출하여 9번/10번 stress 분석을 위한 입력 데이터를 생성하는 단계입니다. STT/WPM이 없는 경우 stress_score는 PENDING으로 유지합니다. |
| meta.audio_file_count | 1000 |
| meta.decode_ok_file_count | 1000 |
| meta.decode_fail_file_count | 0 |
| meta.energy_complete_count | 57611 |
| meta.pitch_complete_count | 53098 |
| meta.feature_complete_count | 0 |
| meta.acoustic_partial_complete_count | 57611 |
| meta.stress_complete_count | 0 |
| outputs.status_json_v205 | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\segment_feature_stress_builder_status_v205.json |
| outputs.merge_status_json_v203_alias | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\segment_feature_stress_merge_status_v203.json |
| outputs.segment_skeleton_csv | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\segment_level_merge_skeleton_v203.csv |
| outputs.segment_level_features_input_v205_csv | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\segment_level_features_input_v205.csv |
| outputs.segment_level_features_input_v203_alias_csv | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\segment_level_features_input_v203.csv |
| outputs.merged_csv_v205 | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\segment_level_features_stress_merged_v205.csv |
| outputs.merged_csv_v203_alias | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\segment_level_features_stress_merged_v203.csv |
| outputs.high_stress_ready_csv | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\high_stress_segment_ready_v203.csv |
| outputs.pair_stress_ready_csv | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\s0_s1_pair_stress_ready_v203.csv |
| outputs.mobile_report_html_v205 | C:\AI\01.sci_voc_bot\reports\research_console\segment_level_feature_stress_merge_v205.html |
| outputs.mobile_report_html_v203_alias | C:\AI\01.sci_voc_bot\reports\research_console\segment_level_feature_stress_merge_v203.html |
| mobile_build_exit_code | 0 |
| mobile_build_tail | [SOC-DEPLOY] package built: C:\AI\01.sci_voc_bot\deploy_packages\sci_voc_soc_mobile_n1000_20260711_083600.zip [SOC-DEPLOY] size=12776717 sha256=726440975683870d... paramiko ready [SOC-DEPLOY] auth=password-auth, user=hdyoo, password=y******!, host=1.234.75.139:22 [SOC-DEPLOY] verifying deployed package [SOC-DEPLOY] local summary rows: 1000 1000 1000 READY [SOC-DEPLOY] http summary rows: 1000 1000 1000 READY DEPLOY_OK |
산출물 JSON: research_continuity/27_segment_feature_stress_merge_v203/segment_feature_stress_merge_status_v203.json
전체 행 포함
| 경로 | 값 |
|---|---|
| version | v205_segment_feature_stress_input_builder |
| generated_at | 2026-07-11 08:36:00 |
| menu_no | 11 |
| menu_label | Segment-level Feature/Stress Input Builder |
| state | DONE_SEGMENT_FEATURE_INPUT__STRESS_PENDING_STT_WPM |
| call_count | 1000 |
| segment_count | 57619 |
| speaker_side_record_count | 2000 |
| generated_input_count | 57619 |
| matched_segment_count | 57619 |
| energy_complete_count | 57611 |
| pitch_complete_count | 53098 |
| wpm_complete_count | 0 |
| stress_complete_count | 0 |
| pending_stress_count | 57619 |
| with_pitch | True |
| allow_acoustic_only_stress | False |
| judgment | segment-level acoustic feature 입력 데이터는 생성되었습니다. 다만 segment-level STT 텍스트가 없어 WPM/stress_score는 PENDING입니다. 다음 단계는 세그먼트 STT 병합입니다. |
| main_paper_replacement | FORBIDDEN |
| customer_agent_claim | FORBIDDEN_UNTIL_ROLE_MAPPING_VALIDATED |
| professor_sentence | 11번은 7번에서 생성한 SPEAKER 세그먼트 57,619건을 기준으로 segment_id를 부여하고, 원 음성에서 세그먼트 단위 duration/energy/pitch(선택)를 산출하여 9번/10번 stress 분석을 위한 입력 데이터를 생성하는 단계입니다. STT/WPM이 없는 경우 stress_score는 PENDING으로 유지합니다. |
| meta.audio_file_count | 1000 |
| meta.decode_ok_file_count | 1000 |
| meta.decode_fail_file_count | 0 |
| meta.energy_complete_count | 57611 |
| meta.pitch_complete_count | 53098 |
| meta.feature_complete_count | 0 |
| meta.acoustic_partial_complete_count | 57611 |
| meta.stress_complete_count | 0 |
| outputs.status_json_v205 | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\segment_feature_stress_builder_status_v205.json |
| outputs.merge_status_json_v203_alias | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\segment_feature_stress_merge_status_v203.json |
| outputs.segment_skeleton_csv | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\segment_level_merge_skeleton_v203.csv |
| outputs.segment_level_features_input_v205_csv | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\segment_level_features_input_v205.csv |
| outputs.segment_level_features_input_v203_alias_csv | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\segment_level_features_input_v203.csv |
| outputs.merged_csv_v205 | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\segment_level_features_stress_merged_v205.csv |
| outputs.merged_csv_v203_alias | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\segment_level_features_stress_merged_v203.csv |
| outputs.high_stress_ready_csv | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\high_stress_segment_ready_v203.csv |
| outputs.pair_stress_ready_csv | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\s0_s1_pair_stress_ready_v203.csv |
| outputs.mobile_report_html_v205 | C:\AI\01.sci_voc_bot\reports\research_console\segment_level_feature_stress_merge_v205.html |
| outputs.mobile_report_html_v203_alias | C:\AI\01.sci_voc_bot\reports\research_console\segment_level_feature_stress_merge_v203.html |
| mobile_build_exit_code | 0 |
| mobile_build_tail | [SOC-DEPLOY] package built: C:\AI\01.sci_voc_bot\deploy_packages\sci_voc_soc_mobile_n1000_20260711_083600.zip [SOC-DEPLOY] size=12776717 sha256=726440975683870d... paramiko ready [SOC-DEPLOY] auth=password-auth, user=hdyoo, password=y******!, host=1.234.75.139:22 [SOC-DEPLOY] verifying deployed package [SOC-DEPLOY] local summary rows: 1000 1000 1000 READY [SOC-DEPLOY] http summary rows: 1000 1000 1000 READY DEPLOY_OK |
산출물 JSON: research_continuity/27_segment_feature_stress_merge_v203/segment_feature_stress_merge_status_v205.json
전체 행 포함
| 경로 | 값 |
|---|---|
| version | v205_segment_feature_stress_input_builder |
| generated_at | 2026-07-11 08:36:00 |
| menu_no | 11 |
| menu_label | Segment-level Feature/Stress Input Builder |
| state | DONE_SEGMENT_FEATURE_INPUT__STRESS_PENDING_STT_WPM |
| call_count | 1000 |
| segment_count | 57619 |
| speaker_side_record_count | 2000 |
| generated_input_count | 57619 |
| matched_segment_count | 57619 |
| energy_complete_count | 57611 |
| pitch_complete_count | 53098 |
| wpm_complete_count | 0 |
| stress_complete_count | 0 |
| pending_stress_count | 57619 |
| with_pitch | True |
| allow_acoustic_only_stress | False |
| judgment | segment-level acoustic feature 입력 데이터는 생성되었습니다. 다만 segment-level STT 텍스트가 없어 WPM/stress_score는 PENDING입니다. 다음 단계는 세그먼트 STT 병합입니다. |
| main_paper_replacement | FORBIDDEN |
| customer_agent_claim | FORBIDDEN_UNTIL_ROLE_MAPPING_VALIDATED |
| professor_sentence | 11번은 7번에서 생성한 SPEAKER 세그먼트 57,619건을 기준으로 segment_id를 부여하고, 원 음성에서 세그먼트 단위 duration/energy/pitch(선택)를 산출하여 9번/10번 stress 분석을 위한 입력 데이터를 생성하는 단계입니다. STT/WPM이 없는 경우 stress_score는 PENDING으로 유지합니다. |
| meta.audio_file_count | 1000 |
| meta.decode_ok_file_count | 1000 |
| meta.decode_fail_file_count | 0 |
| meta.energy_complete_count | 57611 |
| meta.pitch_complete_count | 53098 |
| meta.feature_complete_count | 0 |
| meta.acoustic_partial_complete_count | 57611 |
| meta.stress_complete_count | 0 |
| outputs.status_json_v205 | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\segment_feature_stress_builder_status_v205.json |
| outputs.merge_status_json_v203_alias | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\segment_feature_stress_merge_status_v203.json |
| outputs.segment_skeleton_csv | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\segment_level_merge_skeleton_v203.csv |
| outputs.segment_level_features_input_v205_csv | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\segment_level_features_input_v205.csv |
| outputs.segment_level_features_input_v203_alias_csv | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\segment_level_features_input_v203.csv |
| outputs.merged_csv_v205 | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\segment_level_features_stress_merged_v205.csv |
| outputs.merged_csv_v203_alias | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\segment_level_features_stress_merged_v203.csv |
| outputs.high_stress_ready_csv | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\high_stress_segment_ready_v203.csv |
| outputs.pair_stress_ready_csv | C:\AI\01.sci_voc_bot\research_continuity\27_segment_feature_stress_merge_v203\s0_s1_pair_stress_ready_v203.csv |
| outputs.mobile_report_html_v205 | C:\AI\01.sci_voc_bot\reports\research_console\segment_level_feature_stress_merge_v205.html |
| outputs.mobile_report_html_v203_alias | C:\AI\01.sci_voc_bot\reports\research_console\segment_level_feature_stress_merge_v203.html |
산출물 CSV: research_continuity/27_segment_feature_stress_merge_v203/segment_level_features_input_template_v203.csv (행 500, 열 14)
대용량 표 요약·앞/뒤 표본 포함, 원본은 데이터 ZIP 수록
| segment_id | audio_name | speaker | start_sec | end_sec | duration_sec | text | word_count | energy_mean | pitch_mean | wpm | stress_score | merge_status | role_mapping |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 00__350002022300000__SPEAKER_00__0.031_3.119__000001 | 00__350002022300000.wav | SPEAKER_00 | 0.031 | 3.119 | 3.088 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300000__SPEAKER_01__4.267_7.439__000002 | 00__350002022300000.wav | SPEAKER_01 | 4.267 | 7.439 | 3.172 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300000__SPEAKER_00__4.283_4.570__000003 | 00__350002022300000.wav | SPEAKER_00 | 4.283 | 4.57 | 0.287 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300000__SPEAKER_00__5.768_6.832__000004 | 00__350002022300000.wav | SPEAKER_00 | 5.768 | 6.832 | 1.063 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300000__SPEAKER_01__7.760_10.679__000005 | 00__350002022300000.wav | SPEAKER_01 | 7.76 | 10.679 | 2.919 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300000__SPEAKER_00__10.679_10.797__000006 | 00__350002022300000.wav | SPEAKER_00 | 10.679 | 10.797 | 0.118 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300000__SPEAKER_01__10.797_10.932__000007 | 00__350002022300000.wav | SPEAKER_01 | 10.797 | 10.932 | 0.135 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300000__SPEAKER_01__12.164_19.471__000008 | 00__350002022300000.wav | SPEAKER_01 | 12.164 | 19.471 | 7.307 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300000__SPEAKER_00__19.690_19.977__000009 | 00__350002022300000.wav | SPEAKER_00 | 19.69 | 19.977 | 0.287 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300000__SPEAKER_01__19.977_20.281__000010 | 00__350002022300000.wav | SPEAKER_01 | 19.977 | 20.281 | 0.304 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300000__SPEAKER_01__21.817_28.735__000011 | 00__350002022300000.wav | SPEAKER_01 | 21.817 | 28.735 | 6.919 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300000__SPEAKER_00__28.313_28.347__000012 | 00__350002022300000.wav | SPEAKER_00 | 28.313 | 28.347 | 0.034 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300000__SPEAKER_01__29.123_32.718__000013 | 00__350002022300000.wav | SPEAKER_01 | 29.123 | 32.718 | 3.594 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300000__SPEAKER_00__35.367_36.903__000014 | 00__350002022300000.wav | SPEAKER_00 | 35.367 | 36.903 | 1.536 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300000__SPEAKER_01__38.354_38.405__000015 | 00__350002022300000.wav | SPEAKER_01 | 38.354 | 38.405 | 0.051 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300000__SPEAKER_01__38.911_45.425__000016 | 00__350002022300000.wav | SPEAKER_01 | 38.911 | 45.425 | 6.514 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300000__SPEAKER_00__40.193_40.936__000017 | 00__350002022300000.wav | SPEAKER_00 | 40.193 | 40.936 | 0.742 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300000__SPEAKER_01__45.627_48.040__000018 | 00__350002022300000.wav | SPEAKER_01 | 45.627 | 48.04 | 2.413 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300000__SPEAKER_00__45.779_46.133__000019 | 00__350002022300000.wav | SPEAKER_00 | 45.779 | 46.133 | 0.354 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300000__SPEAKER_01__49.593_54.487__000020 | 00__350002022300000.wav | SPEAKER_01 | 49.593 | 54.487 | 4.894 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300000__SPEAKER_00__54.250_54.470__000021 | 00__350002022300000.wav | SPEAKER_00 | 54.25 | 54.47 | 0.219 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300000__SPEAKER_00__54.487_54.773__000022 | 00__350002022300000.wav | SPEAKER_00 | 54.487 | 54.773 | 0.287 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300000__SPEAKER_01__54.959_56.714__000023 | 00__350002022300000.wav | SPEAKER_01 | 54.959 | 56.714 | 1.755 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300000__SPEAKER_01__56.849_57.220__000024 | 00__350002022300000.wav | SPEAKER_01 | 56.849 | 57.22 | 0.371 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300000__SPEAKER_01__58.925_63.886__000025 | 00__350002022300000.wav | SPEAKER_01 | 58.925 | 63.886 | 4.961 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| ... 중간 450행은 전체 데이터 부록 ZIP에 원본으로 포함 ... | |||||||||||||
| 00__350002022300007__SPEAKER_01__211.610_213.972__000476 | 00__350002022300007.wav | SPEAKER_01 | 211.61 | 213.972 | 2.363 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300007__SPEAKER_00__213.972_216.318__000477 | 00__350002022300007.wav | SPEAKER_00 | 213.972 | 216.318 | 2.346 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300007__SPEAKER_01__216.520_218.377__000478 | 00__350002022300007.wav | SPEAKER_01 | 216.52 | 218.377 | 1.856 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300007__SPEAKER_00__217.988_219.440__000479 | 00__350002022300007.wav | SPEAKER_00 | 217.988 | 219.44 | 1.451 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300007__SPEAKER_01__218.967_220.165__000480 | 00__350002022300007.wav | SPEAKER_01 | 218.967 | 220.165 | 1.198 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300007__SPEAKER_00__219.862_220.047__000481 | 00__350002022300007.wav | SPEAKER_00 | 219.862 | 220.047 | 0.186 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300008__SPEAKER_01__0.082_2.090__000482 | 00__350002022300008.wav | SPEAKER_01 | 0.082 | 2.09 | 2.008 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300008__SPEAKER_00__2.393_4.807__000483 | 00__350002022300008.wav | SPEAKER_00 | 2.393 | 4.807 | 2.413 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300008__SPEAKER_01__4.807_5.600__000484 | 00__350002022300008.wav | SPEAKER_01 | 4.807 | 5.6 | 0.793 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300008__SPEAKER_00__5.566_12.215__000485 | 00__350002022300008.wav | SPEAKER_00 | 5.566 | 12.215 | 6.649 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300008__SPEAKER_01__12.603_14.932__000486 | 00__350002022300008.wav | SPEAKER_01 | 12.603 | 14.932 | 2.329 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300008__SPEAKER_00__14.172_14.476__000487 | 00__350002022300008.wav | SPEAKER_00 | 14.172 | 14.476 | 0.304 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300008__SPEAKER_00__15.353_15.927__000488 | 00__350002022300008.wav | SPEAKER_00 | 15.353 | 15.927 | 0.574 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300008__SPEAKER_00__16.214_17.986__000489 | 00__350002022300008.wav | SPEAKER_00 | 16.214 | 17.986 | 1.772 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300008__SPEAKER_01__18.138_19.218__000490 | 00__350002022300008.wav | SPEAKER_01 | 18.138 | 19.218 | 1.08 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300008__SPEAKER_00__19.437_20.551__000491 | 00__350002022300008.wav | SPEAKER_00 | 19.437 | 20.551 | 1.114 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300008__SPEAKER_01__21.293_22.964__000492 | 00__350002022300008.wav | SPEAKER_01 | 21.293 | 22.964 | 1.671 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300008__SPEAKER_00__22.643_23.673__000493 | 00__350002022300008.wav | SPEAKER_00 | 22.643 | 23.673 | 1.029 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300008__SPEAKER_01__23.977_25.411__000494 | 00__350002022300008.wav | SPEAKER_01 | 23.977 | 25.411 | 1.434 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300008__SPEAKER_00__25.698_26.137__000495 | 00__350002022300008.wav | SPEAKER_00 | 25.698 | 26.137 | 0.439 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300008__SPEAKER_01__53.812_58.385__000496 | 00__350002022300008.wav | SPEAKER_01 | 53.812 | 58.385 | 4.573 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300008__SPEAKER_00__58.621_60.140__000497 | 00__350002022300008.wav | SPEAKER_00 | 58.621 | 60.14 | 1.519 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300008__SPEAKER_01__60.207_61.608__000498 | 00__350002022300008.wav | SPEAKER_01 | 60.207 | 61.608 | 1.401 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300008__SPEAKER_01__61.675_67.278__000499 | 00__350002022300008.wav | SPEAKER_01 | 61.675 | 67.278 | 5.602 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING | ||
| 00__350002022300008__SPEAKER_00__66.012_68.324__000500 | 00__350002022300008.wav | SPEAKER_00 | 66.012 | 68.324 | 2.312 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | SKELETON_ONLY_PENDING_FEATURE_MERGE | PENDING |
산출물 CSV: research_continuity/27_segment_feature_stress_merge_v203/segment_level_features_input_v203.csv (행 57,619, 열 20)
대용량 표 요약·앞/뒤 표본 포함, 원본은 데이터 ZIP 수록
| segment_id | audio_name | audio_path | exists | speaker | start_sec | end_sec | duration_sec | sample_rate | channels | probe_status | decode_status | text | word_count | energy_mean | pitch_mean | wpm | stress_score | stress_formula_status | merge_status |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 00__350002022300000__SPEAKER_00__0.031_3.119__000001 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 0.031 | 3.119 | 3.088 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05476243 | 224.507624 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__4.267_7.439__000002 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 4.267 | 7.439 | 3.172 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05717194 | 136.178324 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__4.283_4.570__000003 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 4.283 | 4.57 | 0.287 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.07147063 | 142.300568 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__5.768_6.832__000004 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 5.768 | 6.832 | 1.063 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05953735 | 174.046484 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__7.760_10.679__000005 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 7.76 | 10.679 | 2.919 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.04357441 | 128.625750 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__10.679_10.797__000006 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 10.679 | 10.797 | 0.118 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.06676706 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__10.797_10.932__000007 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 10.797 | 10.932 | 0.135 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.03883868 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__12.164_19.471__000008 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 12.164 | 19.471 | 7.307 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05996937 | 143.216607 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__19.690_19.977__000009 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 19.69 | 19.977 | 0.287 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.07009622 | 184.422334 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__19.977_20.281__000010 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 19.977 | 20.281 | 0.304 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.06258495 | 269.952568 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__21.817_28.735__000011 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 21.817 | 28.735 | 6.919 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.04108795 | 132.140551 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__28.313_28.347__000012 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 28.313 | 28.347 | 0.034 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.00194903 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__29.123_32.718__000013 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 29.123 | 32.718 | 3.594 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.04556859 | 143.746137 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__35.367_36.903__000014 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 35.367 | 36.903 | 1.536 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.11377881 | 211.708640 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__38.354_38.405__000015 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 38.354 | 38.405 | 0.051 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.00384756 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__38.911_45.425__000016 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 38.911 | 45.425 | 6.514 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.06293251 | 137.944049 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__40.193_40.936__000017 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 40.193 | 40.936 | 0.742 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.13592906 | 156.415997 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__45.627_48.040__000018 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 45.627 | 48.04 | 2.413 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05594229 | 161.496273 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__45.779_46.133__000019 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 45.779 | 46.133 | 0.354 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.06075990 | 154.587695 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__49.593_54.487__000020 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 49.593 | 54.487 | 4.894 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05677043 | 129.793354 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__54.250_54.470__000021 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 54.25 | 54.47 | 0.219 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.06108660 | 140.856160 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__54.487_54.773__000022 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 54.487 | 54.773 | 0.287 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05895352 | 138.758865 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__54.959_56.714__000023 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 54.959 | 56.714 | 1.755 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05213411 | 137.820678 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__56.849_57.220__000024 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 56.849 | 57.22 | 0.371 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05158553 | 164.384858 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__58.925_63.886__000025 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 58.925 | 63.886 | 4.961 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.04586577 | 127.484054 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| ... 중간 57,569행은 전체 데이터 부록 ZIP에 원본으로 포함 ... | |||||||||||||||||||
| 09__350002022300998__SPEAKER_00__4.351_7.034__057595 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_00 | 4.351 | 7.034 | 2.683 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05947952 | 177.129636 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_01__5.954_6.022__057596 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_01 | 5.954 | 6.022 | 0.068 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.00610556 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_01__6.680_6.933__057597 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_01 | 6.68 | 6.933 | 0.253 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.07927645 | 167.599624 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_00__7.810_10.257__057598 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_00 | 7.81 | 10.257 | 2.447 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.03669750 | 172.815121 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_00__10.882_12.299__057599 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_00 | 10.882 | 12.299 | 1.418 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.02106520 | 172.753561 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_00__13.109_13.970__057600 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_00 | 13.109 | 13.97 | 0.861 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05715729 | 287.817050 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_01__13.970_15.286__057601 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_01 | 13.97 | 15.286 | 1.316 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.06276976 | 179.195155 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_00__15.657_16.298__057602 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_00 | 15.657 | 16.298 | 0.641 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05317324 | 224.925860 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_01__15.961_23.723__057603 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_01 | 15.961 | 23.723 | 7.762 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05872604 | 194.641237 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_00__23.723_24.415__057604 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_00 | 23.723 | 24.415 | 0.692 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.01880802 | 167.168043 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_01__32.920_41.155__057605 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_01 | 32.92 | 41.155 | 8.235 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.09712750 | 220.731528 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_00__42.978_43.113__057606 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_00 | 42.978 | 43.113 | 0.135 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.00346261 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_01__43.366_44.767__057607 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_01 | 43.366 | 44.767 | 1.401 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.09667852 | 273.622648 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_00__0.773_1.195__057608 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_00 | 0.773 | 1.195 | 0.422 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.11174220 | 183.448361 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_01__1.432_5.448__057609 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_01 | 1.432 | 5.448 | 4.016 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.07137586 | 173.691773 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_00__5.988_6.292__057610 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_00 | 5.988 | 6.292 | 0.304 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.03353125 | 107.397692 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_01__6.612_16.231__057611 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_01 | 6.612 | 16.231 | 9.619 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.08224404 | 163.989266 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_01__16.552_20.888__057612 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_01 | 16.552 | 20.888 | 4.337 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.07872391 | 178.774233 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_00__20.365_20.635__057613 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_00 | 20.365 | 20.635 | 0.27 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.09700250 | 184.390913 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_01__21.209_24.027__057614 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_01 | 21.209 | 24.027 | 2.818 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.07736503 | 163.670814 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_00__24.027_24.837__057615 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_00 | 24.027 | 24.837 | 0.81 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.07804845 | 175.982792 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_00__25.225_31.165__057616 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_00 | 25.225 | 31.165 | 5.94 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.07197413 | 158.798049 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_01__27.470_28.145__057617 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_01 | 27.47 | 28.145 | 0.675 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.09538542 | 174.212847 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_01__31.756_32.887__057618 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_01 | 31.756 | 32.887 | 1.131 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.02995122 | 134.928754 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_00__32.887_34.405__057619 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_00 | 32.887 | 34.405 | 1.519 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.06122714 | 153.440908 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY |
산출물 CSV: research_continuity/27_segment_feature_stress_merge_v203/segment_level_features_input_v205.csv (행 57,619, 열 20)
대용량 표 요약·앞/뒤 표본 포함, 원본은 데이터 ZIP 수록
| segment_id | audio_name | audio_path | exists | speaker | start_sec | end_sec | duration_sec | sample_rate | channels | probe_status | decode_status | text | word_count | energy_mean | pitch_mean | wpm | stress_score | stress_formula_status | merge_status |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 00__350002022300000__SPEAKER_00__0.031_3.119__000001 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 0.031 | 3.119 | 3.088 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05476243 | 224.507624 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__4.267_7.439__000002 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 4.267 | 7.439 | 3.172 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05717194 | 136.178324 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__4.283_4.570__000003 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 4.283 | 4.57 | 0.287 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.07147063 | 142.300568 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__5.768_6.832__000004 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 5.768 | 6.832 | 1.063 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05953735 | 174.046484 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__7.760_10.679__000005 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 7.76 | 10.679 | 2.919 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.04357441 | 128.625750 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__10.679_10.797__000006 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 10.679 | 10.797 | 0.118 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.06676706 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__10.797_10.932__000007 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 10.797 | 10.932 | 0.135 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.03883868 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__12.164_19.471__000008 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 12.164 | 19.471 | 7.307 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05996937 | 143.216607 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__19.690_19.977__000009 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 19.69 | 19.977 | 0.287 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.07009622 | 184.422334 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__19.977_20.281__000010 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 19.977 | 20.281 | 0.304 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.06258495 | 269.952568 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__21.817_28.735__000011 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 21.817 | 28.735 | 6.919 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.04108795 | 132.140551 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__28.313_28.347__000012 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 28.313 | 28.347 | 0.034 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.00194903 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__29.123_32.718__000013 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 29.123 | 32.718 | 3.594 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.04556859 | 143.746137 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__35.367_36.903__000014 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 35.367 | 36.903 | 1.536 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.11377881 | 211.708640 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__38.354_38.405__000015 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 38.354 | 38.405 | 0.051 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.00384756 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__38.911_45.425__000016 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 38.911 | 45.425 | 6.514 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.06293251 | 137.944049 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__40.193_40.936__000017 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 40.193 | 40.936 | 0.742 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.13592906 | 156.415997 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__45.627_48.040__000018 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 45.627 | 48.04 | 2.413 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05594229 | 161.496273 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__45.779_46.133__000019 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 45.779 | 46.133 | 0.354 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.06075990 | 154.587695 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__49.593_54.487__000020 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 49.593 | 54.487 | 4.894 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05677043 | 129.793354 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__54.250_54.470__000021 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 54.25 | 54.47 | 0.219 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.06108660 | 140.856160 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__54.487_54.773__000022 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 54.487 | 54.773 | 0.287 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05895352 | 138.758865 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__54.959_56.714__000023 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 54.959 | 56.714 | 1.755 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05213411 | 137.820678 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__56.849_57.220__000024 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 56.849 | 57.22 | 0.371 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05158553 | 164.384858 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__58.925_63.886__000025 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 58.925 | 63.886 | 4.961 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.04586577 | 127.484054 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| ... 중간 57,569행은 전체 데이터 부록 ZIP에 원본으로 포함 ... | |||||||||||||||||||
| 09__350002022300998__SPEAKER_00__4.351_7.034__057595 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_00 | 4.351 | 7.034 | 2.683 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05947952 | 177.129636 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_01__5.954_6.022__057596 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_01 | 5.954 | 6.022 | 0.068 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.00610556 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_01__6.680_6.933__057597 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_01 | 6.68 | 6.933 | 0.253 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.07927645 | 167.599624 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_00__7.810_10.257__057598 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_00 | 7.81 | 10.257 | 2.447 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.03669750 | 172.815121 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_00__10.882_12.299__057599 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_00 | 10.882 | 12.299 | 1.418 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.02106520 | 172.753561 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_00__13.109_13.970__057600 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_00 | 13.109 | 13.97 | 0.861 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05715729 | 287.817050 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_01__13.970_15.286__057601 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_01 | 13.97 | 15.286 | 1.316 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.06276976 | 179.195155 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_00__15.657_16.298__057602 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_00 | 15.657 | 16.298 | 0.641 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05317324 | 224.925860 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_01__15.961_23.723__057603 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_01 | 15.961 | 23.723 | 7.762 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05872604 | 194.641237 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_00__23.723_24.415__057604 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_00 | 23.723 | 24.415 | 0.692 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.01880802 | 167.168043 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_01__32.920_41.155__057605 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_01 | 32.92 | 41.155 | 8.235 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.09712750 | 220.731528 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_00__42.978_43.113__057606 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_00 | 42.978 | 43.113 | 0.135 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.00346261 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_01__43.366_44.767__057607 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_01 | 43.366 | 44.767 | 1.401 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.09667852 | 273.622648 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_00__0.773_1.195__057608 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_00 | 0.773 | 1.195 | 0.422 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.11174220 | 183.448361 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_01__1.432_5.448__057609 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_01 | 1.432 | 5.448 | 4.016 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.07137586 | 173.691773 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_00__5.988_6.292__057610 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_00 | 5.988 | 6.292 | 0.304 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.03353125 | 107.397692 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_01__6.612_16.231__057611 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_01 | 6.612 | 16.231 | 9.619 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.08224404 | 163.989266 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_01__16.552_20.888__057612 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_01 | 16.552 | 20.888 | 4.337 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.07872391 | 178.774233 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_00__20.365_20.635__057613 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_00 | 20.365 | 20.635 | 0.27 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.09700250 | 184.390913 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_01__21.209_24.027__057614 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_01 | 21.209 | 24.027 | 2.818 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.07736503 | 163.670814 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_00__24.027_24.837__057615 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_00 | 24.027 | 24.837 | 0.81 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.07804845 | 175.982792 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_00__25.225_31.165__057616 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_00 | 25.225 | 31.165 | 5.94 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.07197413 | 158.798049 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_01__27.470_28.145__057617 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_01 | 27.47 | 28.145 | 0.675 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.09538542 | 174.212847 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_01__31.756_32.887__057618 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_01 | 31.756 | 32.887 | 1.131 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.02995122 | 134.928754 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_00__32.887_34.405__057619 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_00 | 32.887 | 34.405 | 1.519 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.06122714 | 153.440908 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY |
산출물 CSV: research_continuity/27_segment_feature_stress_merge_v203/segment_level_features_stress_merged_v203.csv (행 57,619, 열 20)
대용량 표 요약·앞/뒤 표본 포함, 원본은 데이터 ZIP 수록
| segment_id | audio_name | audio_path | exists | speaker | start_sec | end_sec | duration_sec | sample_rate | channels | probe_status | decode_status | text | word_count | energy_mean | pitch_mean | wpm | stress_score | stress_formula_status | merge_status |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 00__350002022300000__SPEAKER_00__0.031_3.119__000001 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 0.031 | 3.119 | 3.088 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05476243 | 224.507624 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__4.267_7.439__000002 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 4.267 | 7.439 | 3.172 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05717194 | 136.178324 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__4.283_4.570__000003 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 4.283 | 4.57 | 0.287 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.07147063 | 142.300568 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__5.768_6.832__000004 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 5.768 | 6.832 | 1.063 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05953735 | 174.046484 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__7.760_10.679__000005 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 7.76 | 10.679 | 2.919 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.04357441 | 128.625750 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__10.679_10.797__000006 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 10.679 | 10.797 | 0.118 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.06676706 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__10.797_10.932__000007 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 10.797 | 10.932 | 0.135 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.03883868 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__12.164_19.471__000008 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 12.164 | 19.471 | 7.307 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05996937 | 143.216607 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__19.690_19.977__000009 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 19.69 | 19.977 | 0.287 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.07009622 | 184.422334 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__19.977_20.281__000010 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 19.977 | 20.281 | 0.304 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.06258495 | 269.952568 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__21.817_28.735__000011 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 21.817 | 28.735 | 6.919 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.04108795 | 132.140551 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__28.313_28.347__000012 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 28.313 | 28.347 | 0.034 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.00194903 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__29.123_32.718__000013 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 29.123 | 32.718 | 3.594 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.04556859 | 143.746137 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__35.367_36.903__000014 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 35.367 | 36.903 | 1.536 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.11377881 | 211.708640 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__38.354_38.405__000015 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 38.354 | 38.405 | 0.051 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.00384756 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__38.911_45.425__000016 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 38.911 | 45.425 | 6.514 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.06293251 | 137.944049 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__40.193_40.936__000017 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 40.193 | 40.936 | 0.742 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.13592906 | 156.415997 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__45.627_48.040__000018 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 45.627 | 48.04 | 2.413 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05594229 | 161.496273 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__45.779_46.133__000019 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 45.779 | 46.133 | 0.354 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.06075990 | 154.587695 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__49.593_54.487__000020 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 49.593 | 54.487 | 4.894 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05677043 | 129.793354 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__54.250_54.470__000021 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 54.25 | 54.47 | 0.219 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.06108660 | 140.856160 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__54.487_54.773__000022 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 54.487 | 54.773 | 0.287 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05895352 | 138.758865 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__54.959_56.714__000023 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 54.959 | 56.714 | 1.755 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05213411 | 137.820678 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__56.849_57.220__000024 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 56.849 | 57.22 | 0.371 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05158553 | 164.384858 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__58.925_63.886__000025 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 58.925 | 63.886 | 4.961 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.04586577 | 127.484054 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| ... 중간 57,569행은 전체 데이터 부록 ZIP에 원본으로 포함 ... | |||||||||||||||||||
| 09__350002022300998__SPEAKER_00__4.351_7.034__057595 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_00 | 4.351 | 7.034 | 2.683 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05947952 | 177.129636 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_01__5.954_6.022__057596 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_01 | 5.954 | 6.022 | 0.068 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.00610556 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_01__6.680_6.933__057597 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_01 | 6.68 | 6.933 | 0.253 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.07927645 | 167.599624 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_00__7.810_10.257__057598 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_00 | 7.81 | 10.257 | 2.447 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.03669750 | 172.815121 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_00__10.882_12.299__057599 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_00 | 10.882 | 12.299 | 1.418 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.02106520 | 172.753561 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_00__13.109_13.970__057600 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_00 | 13.109 | 13.97 | 0.861 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05715729 | 287.817050 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_01__13.970_15.286__057601 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_01 | 13.97 | 15.286 | 1.316 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.06276976 | 179.195155 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_00__15.657_16.298__057602 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_00 | 15.657 | 16.298 | 0.641 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05317324 | 224.925860 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_01__15.961_23.723__057603 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_01 | 15.961 | 23.723 | 7.762 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05872604 | 194.641237 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_00__23.723_24.415__057604 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_00 | 23.723 | 24.415 | 0.692 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.01880802 | 167.168043 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_01__32.920_41.155__057605 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_01 | 32.92 | 41.155 | 8.235 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.09712750 | 220.731528 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_00__42.978_43.113__057606 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_00 | 42.978 | 43.113 | 0.135 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.00346261 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_01__43.366_44.767__057607 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_01 | 43.366 | 44.767 | 1.401 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.09667852 | 273.622648 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_00__0.773_1.195__057608 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_00 | 0.773 | 1.195 | 0.422 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.11174220 | 183.448361 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_01__1.432_5.448__057609 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_01 | 1.432 | 5.448 | 4.016 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.07137586 | 173.691773 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_00__5.988_6.292__057610 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_00 | 5.988 | 6.292 | 0.304 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.03353125 | 107.397692 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_01__6.612_16.231__057611 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_01 | 6.612 | 16.231 | 9.619 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.08224404 | 163.989266 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_01__16.552_20.888__057612 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_01 | 16.552 | 20.888 | 4.337 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.07872391 | 178.774233 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_00__20.365_20.635__057613 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_00 | 20.365 | 20.635 | 0.27 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.09700250 | 184.390913 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_01__21.209_24.027__057614 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_01 | 21.209 | 24.027 | 2.818 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.07736503 | 163.670814 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_00__24.027_24.837__057615 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_00 | 24.027 | 24.837 | 0.81 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.07804845 | 175.982792 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_00__25.225_31.165__057616 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_00 | 25.225 | 31.165 | 5.94 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.07197413 | 158.798049 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_01__27.470_28.145__057617 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_01 | 27.47 | 28.145 | 0.675 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.09538542 | 174.212847 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_01__31.756_32.887__057618 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_01 | 31.756 | 32.887 | 1.131 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.02995122 | 134.928754 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_00__32.887_34.405__057619 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_00 | 32.887 | 34.405 | 1.519 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.06122714 | 153.440908 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY |
산출물 CSV: research_continuity/27_segment_feature_stress_merge_v203/segment_level_features_stress_merged_v205.csv (행 57,619, 열 20)
대용량 표 요약·앞/뒤 표본 포함, 원본은 데이터 ZIP 수록
| segment_id | audio_name | audio_path | exists | speaker | start_sec | end_sec | duration_sec | sample_rate | channels | probe_status | decode_status | text | word_count | energy_mean | pitch_mean | wpm | stress_score | stress_formula_status | merge_status |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 00__350002022300000__SPEAKER_00__0.031_3.119__000001 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 0.031 | 3.119 | 3.088 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05476243 | 224.507624 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__4.267_7.439__000002 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 4.267 | 7.439 | 3.172 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05717194 | 136.178324 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__4.283_4.570__000003 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 4.283 | 4.57 | 0.287 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.07147063 | 142.300568 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__5.768_6.832__000004 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 5.768 | 6.832 | 1.063 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05953735 | 174.046484 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__7.760_10.679__000005 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 7.76 | 10.679 | 2.919 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.04357441 | 128.625750 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__10.679_10.797__000006 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 10.679 | 10.797 | 0.118 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.06676706 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__10.797_10.932__000007 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 10.797 | 10.932 | 0.135 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.03883868 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__12.164_19.471__000008 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 12.164 | 19.471 | 7.307 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05996937 | 143.216607 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__19.690_19.977__000009 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 19.69 | 19.977 | 0.287 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.07009622 | 184.422334 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__19.977_20.281__000010 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 19.977 | 20.281 | 0.304 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.06258495 | 269.952568 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__21.817_28.735__000011 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 21.817 | 28.735 | 6.919 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.04108795 | 132.140551 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__28.313_28.347__000012 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 28.313 | 28.347 | 0.034 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.00194903 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__29.123_32.718__000013 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 29.123 | 32.718 | 3.594 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.04556859 | 143.746137 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__35.367_36.903__000014 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 35.367 | 36.903 | 1.536 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.11377881 | 211.708640 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__38.354_38.405__000015 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 38.354 | 38.405 | 0.051 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.00384756 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__38.911_45.425__000016 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 38.911 | 45.425 | 6.514 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.06293251 | 137.944049 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__40.193_40.936__000017 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 40.193 | 40.936 | 0.742 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.13592906 | 156.415997 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__45.627_48.040__000018 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 45.627 | 48.04 | 2.413 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05594229 | 161.496273 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__45.779_46.133__000019 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 45.779 | 46.133 | 0.354 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.06075990 | 154.587695 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__49.593_54.487__000020 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 49.593 | 54.487 | 4.894 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05677043 | 129.793354 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__54.250_54.470__000021 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 54.25 | 54.47 | 0.219 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.06108660 | 140.856160 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_00__54.487_54.773__000022 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_00 | 54.487 | 54.773 | 0.287 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05895352 | 138.758865 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__54.959_56.714__000023 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 54.959 | 56.714 | 1.755 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05213411 | 137.820678 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__56.849_57.220__000024 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 56.849 | 57.22 | 0.371 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05158553 | 164.384858 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 00__350002022300000__SPEAKER_01__58.925_63.886__000025 | 00__350002022300000.wav | C:\jupyter_env\VOC_full\00__350002022300000.wav | True | SPEAKER_01 | 58.925 | 63.886 | 4.961 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.04586577 | 127.484054 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| ... 중간 57,569행은 전체 데이터 부록 ZIP에 원본으로 포함 ... | |||||||||||||||||||
| 09__350002022300998__SPEAKER_00__4.351_7.034__057595 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_00 | 4.351 | 7.034 | 2.683 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05947952 | 177.129636 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_01__5.954_6.022__057596 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_01 | 5.954 | 6.022 | 0.068 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.00610556 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_01__6.680_6.933__057597 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_01 | 6.68 | 6.933 | 0.253 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.07927645 | 167.599624 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_00__7.810_10.257__057598 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_00 | 7.81 | 10.257 | 2.447 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.03669750 | 172.815121 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_00__10.882_12.299__057599 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_00 | 10.882 | 12.299 | 1.418 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.02106520 | 172.753561 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_00__13.109_13.970__057600 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_00 | 13.109 | 13.97 | 0.861 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05715729 | 287.817050 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_01__13.970_15.286__057601 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_01 | 13.97 | 15.286 | 1.316 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.06276976 | 179.195155 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_00__15.657_16.298__057602 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_00 | 15.657 | 16.298 | 0.641 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05317324 | 224.925860 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_01__15.961_23.723__057603 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_01 | 15.961 | 23.723 | 7.762 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.05872604 | 194.641237 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_00__23.723_24.415__057604 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_00 | 23.723 | 24.415 | 0.692 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.01880802 | 167.168043 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_01__32.920_41.155__057605 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_01 | 32.92 | 41.155 | 8.235 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.09712750 | 220.731528 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_00__42.978_43.113__057606 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_00 | 42.978 | 43.113 | 0.135 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.00346261 | PENDING_FEATURE_MERGE | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300998__SPEAKER_01__43.366_44.767__057607 | 09__350002022300998.wav | C:\jupyter_env\VOC_full\09__350002022300998.wav | True | SPEAKER_01 | 43.366 | 44.767 | 1.401 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.09667852 | 273.622648 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_00__0.773_1.195__057608 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_00 | 0.773 | 1.195 | 0.422 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.11174220 | 183.448361 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_01__1.432_5.448__057609 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_01 | 1.432 | 5.448 | 4.016 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.07137586 | 173.691773 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_00__5.988_6.292__057610 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_00 | 5.988 | 6.292 | 0.304 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.03353125 | 107.397692 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_01__6.612_16.231__057611 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_01 | 6.612 | 16.231 | 9.619 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.08224404 | 163.989266 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_01__16.552_20.888__057612 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_01 | 16.552 | 20.888 | 4.337 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.07872391 | 178.774233 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_00__20.365_20.635__057613 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_00 | 20.365 | 20.635 | 0.27 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.09700250 | 184.390913 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_01__21.209_24.027__057614 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_01 | 21.209 | 24.027 | 2.818 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.07736503 | 163.670814 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_00__24.027_24.837__057615 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_00 | 24.027 | 24.837 | 0.81 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.07804845 | 175.982792 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_00__25.225_31.165__057616 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_00 | 25.225 | 31.165 | 5.94 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.07197413 | 158.798049 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_01__27.470_28.145__057617 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_01 | 27.47 | 28.145 | 0.675 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.09538542 | 174.212847 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_01__31.756_32.887__057618 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_01 | 31.756 | 32.887 | 1.131 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.02995122 | 134.928754 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY | ||
| 09__350002022300999__SPEAKER_00__32.887_34.405__057619 | 09__350002022300999.wav | C:\jupyter_env\VOC_full\09__350002022300999.wav | True | SPEAKER_00 | 32.887 | 34.405 | 1.519 | 8000 | 1 | OK_FFPROBE | OK_FFMPEG_DECODE | 0.06122714 | 153.440908 | PENDING_FEATURE_MERGE | PENDING_SEGMENT_LEVEL_STRESS_SCORE | PENDING_WPM_OR_FEATURES | PARTIAL_FEATURES_ENERGY_DURATION_READY |
결과 해석
11번의 acoustic feature 입력과 segment_id 병합은 완료됐지만, stress 분석 완료로 볼 수는 없다. 현재 완료 범위는 duration/energy/pitch 입력 생성까지이다.
한계 및 논문 반영 기준
stress_score 완료 0건을 숨기거나 임의 값으로 대체하지 않는다.
12. IEEE Access 신규 학회지 한글 원고 설계
논문 작성용 상세 본문
12번은 TO-BE 1,000건을 독립적인 연구 데이터로 삼고, 이후 완료된 실제 화자분리·화자별 발화 구조·시간순 응답쌍·세그먼트 음향 특징 분석을 중심으로 새로운 IEEE Access 논문을 작성한다. 기존 n=998 결과와 AS-IS/TO-BE 비교는 신규 논문의 연구 범위에 포함하지 않는다.
논문 연구 질문과 결과는 TO-BE 이후 분석에만 집중한다. IEEE 심사를 위해 사용 데이터, 전처리 조건, pyannote 설정, 세그먼트 생성 규칙, 응답쌍 산출 기준과 통계 절차는 연구 방법에 명확히 기록하여 분석 과정의 투명성을 확보한다.
논문은 먼저 한글로 완성한다. 한글 단계에서 제목, 초록, 연구 질문, 변수 정의, 표·그림, 결과 수치와 주장 범위를 확정한 뒤 IEEE Access 템플릿에 맞춰 영문으로 변환한다. 이 순서를 따르면 영문 작성 중 연구 방향과 수치가 반복적으로 흔들리는 문제를 줄일 수 있다.
현재 논문의 핵심 실증 범위는 1,000통화, 57,619개 화자 세그먼트, 2,000개 통화-화자 후보 슬롯, 28,149개 시간순 응답쌍과 세그먼트 duration·energy·pitch이다. 화자별 발화량, 발화 비중, 턴 수, 평균 발화 길이, 응답 지연과 음향 특징 차이를 주요 결과로 구성한다.
SPEAKER_00과 SPEAKER_01은 자동 화자분리에서 생성된 상대적 군집 라벨이다. 별도의 역할 확인 절차가 끝나기 전에는 고객과 상담사로 명명하지 않고, 화자 후보 S0·S1로 기술한다.
세그먼트 단위 STT, WPM, stress_score가 아직 완성되지 않았으므로 스트레스 전이, 고스트레스 반응, 고객 스트레스가 상담사에게 미치는 영향은 이번 논문에서 제외한다. 해당 분석은 데이터가 완성된 뒤 후속 논문 또는 확장 연구로 분리한다.
한글 초록 초안: 본 연구는 실제 콜센터 VOC 음성 1,000건을 대상으로 화자분리 기반의 상담 상호작용 구조를 정량화하는 것을 목적으로 한다. 통화 음성에 화자분리 절차를 적용하여 57,619개의 화자 세그먼트를 생성하고, 통화별 두 개의 화자 후보 슬롯 총 2,000개와 시간 순서에 따른 28,149개의 응답쌍을 구성하였다. 각 화자 후보에 대해 발화시간, 발화 비중, 턴 수, 세그먼트 수와 평균 발화 길이를 산출하고, 응답쌍의 지연시간과 교대 구조를 분석하도록 연구 모형을 설계하였다. 또한 세그먼트 단위 duration과 energy 57,611건, pitch 53,098건을 병합하여 화자 및 상호작용 위치에 따른 음향적 차이를 분석할 수 있는 기반을 마련하였다. 이를 통해 실제 상담 통화 내부의 화자별 발화 구조와 시간순 상호작용 패턴을 정량적으로 설명하고, 통화 전체 단위 분석으로는 확인하기 어려운 대화 구조를 제시하였다. 다만 화자 후보의 고객·상담사 역할은 별도 검증 전까지 확정하지 않으며, 세그먼트 단위 WPM과 스트레스 지수는 본 논문의 결과 범위에서 제외한다.
분석 수치 및 결과표
표 12-1. 신규 IEEE Access 논문 제목 후보
| 구분 | 한글 제목 후보 | 권장도 |
|---|---|---|
| 권장 제목 | 콜센터 VOC 음성의 화자분리 기반 상호작용 구조 분석: 1,000건 실제 상담 데이터의 발화량, 턴 및 응답쌍을 중심으로 | 최우선 |
| 대안 1 | 실제 콜센터 상담 음성에서 화자 후보별 발화 구조와 시간순 응답 패턴 분석 | 간결형 |
| 대안 2 | 화자분리와 세그먼트 음향 특징을 활용한 콜센터 VOC 상호작용 분석 | 방법 중심 |
표 12-2. 신규 논문 포함·제외 범위
| 구분 | 포함 여부 | 논문 반영 기준 |
|---|---|---|
| TO-BE 실제 상담 음성 | 포함 | 1,000건을 신규 논문의 단일 기준 데이터로 사용 |
| 실제 화자분리 결과 | 포함 | 57,619개 세그먼트와 시간 구간 정보 |
| 통화-화자 후보 슬롯 | 포함 | 2,000개 슬롯; 미검출 화자는 0초 슬롯으로 보존 |
| 시간순 응답쌍 | 포함 | 28,149개 응답쌍; 연속 발화 구조와 응답 지연 분석 |
| 세그먼트 음향 특징 | 포함 | duration 전수, energy 57,611건, pitch 53,098건 |
| AS-IS n=998 결과 | 제외 | 이 신규 논문의 결과·비교 기준으로 사용하지 않음 |
| AS-IS/TO-BE 비교 | 제외 | 신규 논문의 연구 문제와 결과에 포함하지 않음 |
| 기존 분석 반복 검증 | 제외 | TO-BE 이후 화자분리·상호작용 분석만 신규 논문에 사용 |
| 고객·상담사 역할 확정 | 보류 | SPEAKER_00/01은 검증 전 화자 후보로 유지 |
| 세그먼트 stress_score | 후속 | STT·WPM 병합 완료 뒤 별도 분석 또는 후속 논문으로 확장 |
| 스트레스 전이·인과 | 제외 | 현재 논문에서는 주장하지 않음 |
표 12-3. 연구 질문
| 연구 질문 | 내용 | 사용 자료/지표 |
|---|---|---|
| RQ1 | 1,000건 실제 상담 음성에서 화자분리 결과는 어떤 규모와 검출 구조를 보이는가? | 통화 수, 세그먼트 수, 화자 수, 미검출 통화 |
| RQ2 | 화자 후보별 발화시간, 발화 비중, 세그먼트 수, 턴 수와 평균 발화 길이는 어떤 차이를 보이는가? | 화자별 기술통계와 통화 내 비율 |
| RQ3 | 시간순 응답쌍에서 응답 지연과 교대 패턴은 어떤 분포를 보이는가? | 응답쌍 수, latency, 전환 유형 |
| RQ4 | 세그먼트의 duration, energy, pitch는 화자 후보 및 상호작용 구조에 따라 어떤 차이를 보이는가? | 세그먼트 음향 특징, 집단 비교, 효과크기 |
| 후속 RQ | 역할 매핑과 세그먼트 STT·WPM·stress가 완료된 뒤 스트레스 반응 구조가 나타나는가? | 현재 논문 결과가 아닌 후속 연구 |
표 12-4. 현재 데이터와 분석 단위
| 항목 | 현재 값 | 논문 사용 상태 |
|---|---|---|
| TO-BE 통화 | 1,000 | 핵심 표본 |
| 화자 세그먼트 | 57,619 | 핵심 분석 단위 |
| 통화-화자 슬롯 | 2,000 | 화자 후보 비교 |
| S0 검출 통화 | 1,000 | 기술통계 |
| S1 검출 통화 | 960 | 기술통계 |
| S1 미검출 통화 | 40 | 0초 슬롯 보존 및 민감도 점검 |
| 시간순 응답쌍 | 28,149 | 응답 패턴 분석 |
| segment_id 매칭 | 57,619 | 전수 매칭 |
| energy 완료 | 57,611 | 분석 가능 |
| pitch 완료 | 53,098 | 무성음·초단구간 결측 고려 |
| segment WPM | 0 | 현재 논문에서 제외 |
| segment stress_score | 0 | 현재 논문에서 제외 |
표 12-5. 한글 논문 권장 구성
| 순서 | 한글 원고 장 | 작성 내용 |
|---|---|---|
| 1 | 제목·저자·소속 | 화자분리 기반 상호작용 구조 분석이라는 독립 연구 주제를 명확히 표시 |
| 2 | 초록 | 연구 목적, 1,000건 데이터, 화자분리, 구조 지표, 핵심 결과와 기여를 한 문단으로 작성 |
| 3 | 주제어 | 콜센터 분석, 화자분리, 대화 상호작용, 음향 특징, VOC 음성 등 3~10개 |
| 4 | 1. 서론 | 문제 배경, 기존 연구 한계, 연구 필요성, 연구 질문과 기여 |
| 5 | 2. 관련 연구 | 화자분리, 콜센터 대화 분석, 턴테이킹, 응답 지연, 음향 특징 연구 |
| 6 | 3. 연구 방법 | 데이터·윤리, 전처리, 화자분리, 슬롯 구성, 응답쌍, 음향 특징, 통계 방법 |
| 7 | 4. 분석 결과 | 화자분리 규모, 화자별 발화 구조, 응답쌍, 응답 지연, 음향 특징 결과 |
| 8 | 5. 논의 | 상담 상호작용 구조의 의미, 기술적 기여, 현장 적용 가능성, 선행연구 비교 |
| 9 | 6. 한계 및 윤리 | 역할 미확정, 자동 화자분리 오류, 단일 기관 자료, 개인정보와 비식별화 |
| 10 | 7. 결론 | 검증된 구조 결과와 향후 역할 매핑·세그먼트 스트레스 연구를 구분해 요약 |
| 11 | 감사의 글·참고문헌·저자 약력 | IEEE 숫자 인용과 모든 저자 약력 포함 |
표 12-6. 분석 계획과 결과 보고 기준
| 분석 영역 | 권장 분석 | 결과 보고 방식 |
|---|---|---|
| 화자분리 품질 | 통화별 화자 수, 세그먼트 수, 무발화·단일화자 통화 점검 | 건수·비율과 오류 유형 |
| 화자별 발화 구조 | 총 발화시간, 발화 비중, 턴 수, 세그먼트 수, 평균/중앙 발화 길이 | 평균·SD·중앙값·IQR |
| 화자 후보 비교 | 대응표본 검정 또는 비모수 검정, 효과크기 | p값만이 아니라 효과크기와 신뢰구간 |
| 응답쌍 | 응답 지연 분포, 전환 빈도, 장·단 지연 구간 | 히스토그램·백분위수·강건 통계 |
| 음향 특징 | duration, energy, pitch의 화자 후보·턴 위치별 비교 | 결측률과 무성음 처리 기준 병기 |
| 민감도 분석 | S1 미검출 40건 포함/제외, 초단 세그먼트 기준 변경 | 주요 결론 유지 여부 |
| 역할·스트레스 | 현재 분석에서 제외 | 후속 연구로 명시 |
표 12-7. 과장 방지 및 주장 표현 규칙
| 표현 | 사용 여부 | 권장 문구 |
|---|---|---|
| 기존 결과 재검증 | 사용하지 않음 | TO-BE 기반 신규 화자분리 연구로 표현 |
| 고객/상담사 | 검증 전 사용 금지 | 화자 후보 S0/S1 또는 SPEAKER_00/01 |
| 영향·인과 | 사용 금지 | 차이, 연관, 시간순 패턴, 탐색적 관계 |
| 스트레스 전이 | 현재 사용 금지 | 세그먼트 stress 완성 후 후속 검증 필요 |
| 화자분리 정확도 | 정답 라벨 없으면 정확도 표현 금지 | 분리 결과 규모와 품질 진단으로 표현 |
| 새로운 기여 | 사용 가능 | 실제 콜센터 1,000건에서 화자 구조와 응답쌍을 정량화한 방법·결과 |
표 12-8. IEEE Access 제출 규칙 한글 요약
| 번호 | IEEE Access 투고 규칙 | 현재 적용 |
|---|---|---|
| 1 | 공식 2단·단일 줄간격 템플릿 사용 | 한글 초안 완료 뒤 공식 Word 템플릿에 영문화 |
| 2 | Word/LaTeX 원본과 PDF 동시 제출 및 내용 일치 | 최종 제출 단계에서 대조 |
| 3 | 파일당 40MB 이하 | 최종 Word/PDF 용량 점검 |
| 4 | 저자 자격·순서·ORCID 확정 | 투고 전 확정 |
| 5 | 모든 저자의 짧은 약력 포함 | 참고문헌 뒤에 배치 |
| 6 | 영문 문법과 철자 검수 | 한글 원고 확정 후 전문 영문 교정 |
| 7 | 관련 참고문헌 정확성·철회 여부 점검 | DOI와 철회 문헌 확인 |
| 8 | 약어는 본문 첫 사용 시 정의 | STT, VOC, RMS, F0 등을 각각 정의 |
| 9 | 키워드 3~10개 선택 | 영문 원고에서 알파벳순 정리 |
| 10 | 20페이지 이하 권장 | 구조 결과 중심으로 압축하고 상세 표는 보충자료로 이동 |
표 12-9. 신규 논문 작성 준비 상태
| 작업 항목 | 상태 | 다음 작업 |
|---|---|---|
| 신규 연구 범위 | 확정 | TO-BE 및 7~11번 중 완료 결과만 사용 |
| 한글 제목·목차 | 확정 | 교수 검토 후 문구 조정 |
| 화자분리 데이터 | READY | 57,619개 세그먼트 결과표 정리 |
| 화자 슬롯·응답쌍 | READY | 화자별 기술통계와 latency 결과 생성 |
| 세그먼트 음향 특징 | PARTIAL READY | energy/pitch 결측률과 집단 비교 |
| 역할 매핑 | PENDING | 검증 전 S0/S1 후보 라벨 유지 |
| 세그먼트 WPM/stress | 논문 범위 제외 | 완료 뒤 후속 연구로 분리 |
| 한글 결과 본문 | PENDING | 실제 통계표를 기준으로 작성 |
| 영문 변환 | 후속 | 한글 원고와 수치 확정 후 진행 |
| ORCID·저자 약력 | PENDING | 제출 직전 확정 |
분석 그림 및 도식
원본 분석 산출물 연계
해당 장과 직접 매칭되는 추가 CSV/JSON/텍스트 산출물이 없습니다.
결과 해석
신규 IEEE Access 논문의 중심은 실제 상담 통화 내부의 화자별 발화 구조와 시간순 상호작용을 정량화하는 것이다. TO-BE 1,000건을 단일 연구 기준으로 사용하고, 화자분리·화자 슬롯·응답쌍·세그먼트 음향 특징의 완료 결과를 본문에 배치한다. 미완료된 역할 매핑과 세그먼트 스트레스는 결과에서 제외하여 현재 데이터만으로 독립적이고 과장 없는 논문 구조를 확보한다.
한계 및 논문 반영 기준
신규 논문의 범위를 TO-BE 이후 분석으로 제한하더라도 연구 방법의 투명성은 유지해야 한다. 사용 모델과 버전, 파라미터, 전처리, 통계 방법을 구체적으로 기록한다. 또한 화자분리 정답 라벨이 없다면 '화자분리 정확도'를 주장할 수 없고, 고객·상담사 역할과 스트레스 영향도 현재 결과로 확정할 수 없다.
신규 학회지 한글 원고 및 IEEE Access 자료 다운로드
부록 A. 전체 분석 산출물 파일 목록
모든 발견 산출물은 별도 데이터 ZIP에 원본 그대로 포함된다.
| 번호 | 장 | 파일 | 형식 | 크기(bytes) | 행 | 열/키 | 이미지크기 | SHA256(앞16) |
|---|---|---|---|---|---|---|---|---|
| 1 | 1 | reports/figures/deploy_visuals/to_be_actual_visual_summary.png | png | 232,717 | 1906x1214 | 00c6a9562826ecef | ||
| 2 | 1 | reports/research_console/.v213_tmp/html_ref_VOC_0413_분석리포트_v5_with_images_001.png | png | 88,937 | 1258x885 | 084e604b356c81a6 | ||
| 3 | 1 | reports/research_console/.v213_tmp/html_ref_VOC_0413_분석리포트_v5_with_images_002.png | png | 104,478 | 1258x883 | 43f4052238d8ebd9 | ||
| 4 | 1 | reports/research_console/.v213_tmp/html_ref_VOC_0413_분석리포트_v5_with_images_003.png | png | 97,741 | 1257x883 | 03bd14e4bab2e528 | ||
| 5 | 1 | reports/research_console/.v213_tmp/html_ref_VOC_0413_분석리포트_v5_with_images_004.png | png | 86,929 | 1258x883 | 1a9a187637c166ba | ||
| 6 | 1 | reports/research_console/.v213_tmp/html_ref_VOC_0413_분석리포트_v5_with_images_005.png | png | 105,214 | 1258x884 | 675239ceaf491d7f | ||
| 7 | 1 | reports/research_console/.v213_tmp/html_ref_VOC_0413_분석리포트_v5_with_images_006.png | png | 99,078 | 1557x887 | a40804f8cde3dcb2 | ||
| 8 | 1 | reports/research_console/.v213_tmp/html_ref_VOC_0413_분석리포트_v5_with_images_007.png | png | 117,550 | 1783x913 | e3c6250601fda513 | ||
| 9 | 1 | reports/research_console/.v213_tmp/html_ref_VOC_0413_분석리포트_v5_with_images_008.png | png | 73,332 | 1258x879 | 036d080d37dc6970 | ||
| 10 | 1 | reports/research_console/.v213_tmp/html_ref_VOC_0413_분석리포트_v5_with_images_009.png | png | 74,648 | 1257x879 | de3a1d6e63d34b34 | ||
| 11 | 1 | reports/research_console/.v213_tmp/html_ref_VOC_0413_분석리포트_v5_with_images_010.png | png | 68,785 | 1258x879 | 43926d49a66d2034 | ||
| 12 | 1 | reports/research_console/.v213_tmp/html_ref_VOC_0413_분석리포트_v5_with_images_011.png | png | 69,235 | 1259x879 | b22bb611876741a2 | ||
| 13 | 1 | reports/research_console/.v213_tmp/ref_VOC_0413_분석리포트_v5_with_images_001.png | png | 88,937 | 1258x885 | 084e604b356c81a6 | ||
| 14 | 1 | reports/research_console/.v213_tmp/ref_VOC_0413_분석리포트_v5_with_images_002.png | png | 104,478 | 1258x883 | 43f4052238d8ebd9 | ||
| 15 | 1 | reports/research_console/.v213_tmp/ref_VOC_0413_분석리포트_v5_with_images_003.png | png | 97,741 | 1257x883 | 03bd14e4bab2e528 | ||
| 16 | 1 | reports/research_console/.v213_tmp/ref_VOC_0413_분석리포트_v5_with_images_004.png | png | 86,929 | 1258x883 | 1a9a187637c166ba | ||
| 17 | 1 | reports/research_console/.v213_tmp/ref_VOC_0413_분석리포트_v5_with_images_005.png | png | 105,214 | 1258x884 | 675239ceaf491d7f | ||
| 18 | 1 | reports/research_console/.v213_tmp/ref_VOC_0413_분석리포트_v5_with_images_006.png | png | 99,078 | 1557x887 | a40804f8cde3dcb2 | ||
| 19 | 1 | reports/research_console/.v213_tmp/ref_VOC_0413_분석리포트_v5_with_images_007.png | png | 117,550 | 1783x913 | e3c6250601fda513 | ||
| 20 | 1 | reports/research_console/.v213_tmp/ref_VOC_0413_분석리포트_v5_with_images_008.png | png | 73,332 | 1258x879 | 036d080d37dc6970 | ||
| 21 | 1 | reports/research_console/.v213_tmp/ref_VOC_0413_분석리포트_v5_with_images_009.png | png | 74,648 | 1257x879 | de3a1d6e63d34b34 | ||
| 22 | 1 | reports/research_console/.v213_tmp/ref_VOC_0413_분석리포트_v5_with_images_010.png | png | 68,785 | 1258x879 | 43926d49a66d2034 | ||
| 23 | 1 | reports/research_console/.v213_tmp/ref_VOC_0413_분석리포트_v5_with_images_011.png | png | 69,235 | 1259x879 | b22bb611876741a2 | ||
| 24 | 1 | reports/research_console/.v213_tmp/ref_유현동VOC_datasets0402_분석리포트_v3_001.png | png | 47,938 | 1200x750 | 001fb03ea94a7669 | ||
| 25 | 1 | reports/research_console/.v213_tmp/ref_유현동VOC_datasets0402_분석리포트_v3_002.png | png | 43,385 | 1200x750 | c28c9370f116b3fd | ||
| 26 | 1 | reports/research_console/.v213_tmp/ref_유현동VOC_datasets0402_분석리포트_v3_003.png | png | 55,664 | 1500x750 | 28f9c0bb5d4aaf49 | ||
| 27 | 1 | reports/research_console/.v213_tmp/ref_유현동VOC_datasets0402_분석리포트_v3_004.png | png | 48,577 | 1200x750 | 86ac4bc2a8c6490a | ||
| 28 | 1 | reports/research_console/.v213_tmp/ref_유현동VOC_연구_중간보고_001.jpg | jpg | 67,701 | 1400x1200 | 329b9ee976173c8c | ||
| 29 | 1 | reports/research_console/.v213_tmp/ref_유현동VOC_연구_중간보고_002.jpg | jpg | 79,416 | 1400x1200 | 08dc33b5062059be | ||
| 30 | 1 | reports/research_console/.v213_tmp/ref_유현동VOC_연구_중간보고_003.jpg | jpg | 128,009 | 1280x960 | 0556ebc07d71a9c4 | ||
| 31 | 1 | reports/research_console/.v213_tmp/ref_유현동VOC_연구_중간보고_004.jpg | jpg | 45,227 | 1280x960 | 845c6ad4f0213bc5 | ||
| 32 | 1 | reports/research_console/.v213_tmp/ref_유현동VOC_연구_중간보고_005.jpg | jpg | 80,206 | 1800x900 | c14193ed8f0633a0 | ||
| 33 | 1 | reports/research_console/.v213_tmp/ref_유현동VOC_연구_중간보고_006.jpg | jpg | 81,670 | 1440x900 | 57e71eca26ee9ff3 | ||
| 34 | 1 | reports/research_console/.v213_tmp/ref_유현동VOC_연구_중간보고_007.jpg | jpg | 196,823 | 2000x1368 | e2dd32fe3eefbfee | ||
| 35 | 1 | reports/research_console/.v213_tmp/ref_유현동VOC_연구_중간보고_008.jpg | jpg | 59,981 | 1440x900 | 4abd8c57f997a021 | ||
| 36 | 1 | reports/research_console/.v213_tmp/ref_유현동VOC_연구_중간보고_009.jpg | jpg | 148,836 | 2000x1333 | 40504543153c15b6 | ||
| 37 | 1 | reports/research_console/.v213_tmp/ref_유현동VOC_연구_중간보고_010.jpg | jpg | 107,564 | 1218x1110 | 9e2d5b92ecb50b4a | ||
| 38 | 1 | reports/research_console/.v213_tmp/ref_유현동VOC_연구_중간보고_011.jpg | jpg | 73,374 | 1440x900 | e39d0c1159393cc9 | ||
| 39 | 1 | reports/research_console/.v215_tmp/html_ref_VOC_0413_분석리포트_v5_with_images_001.png | png | 88,937 | 1258x885 | 084e604b356c81a6 | ||
| 40 | 1 | reports/research_console/.v215_tmp/html_ref_VOC_0413_분석리포트_v5_with_images_002.png | png | 104,478 | 1258x883 | 43f4052238d8ebd9 | ||
| 41 | 1 | reports/research_console/.v215_tmp/html_ref_VOC_0413_분석리포트_v5_with_images_003.png | png | 97,741 | 1257x883 | 03bd14e4bab2e528 | ||
| 42 | 1 | reports/research_console/.v215_tmp/html_ref_VOC_0413_분석리포트_v5_with_images_004.png | png | 86,929 | 1258x883 | 1a9a187637c166ba | ||
| 43 | 1 | reports/research_console/.v215_tmp/html_ref_VOC_0413_분석리포트_v5_with_images_005.png | png | 105,214 | 1258x884 | 675239ceaf491d7f | ||
| 44 | 1 | reports/research_console/.v215_tmp/html_ref_VOC_0413_분석리포트_v5_with_images_006.png | png | 99,078 | 1557x887 | a40804f8cde3dcb2 | ||
| 45 | 1 | reports/research_console/.v215_tmp/html_ref_VOC_0413_분석리포트_v5_with_images_007.png | png | 117,550 | 1783x913 | e3c6250601fda513 | ||
| 46 | 1 | reports/research_console/.v215_tmp/html_ref_VOC_0413_분석리포트_v5_with_images_008.png | png | 73,332 | 1258x879 | 036d080d37dc6970 | ||
| 47 | 1 | reports/research_console/.v215_tmp/html_ref_VOC_0413_분석리포트_v5_with_images_009.png | png | 74,648 | 1257x879 | de3a1d6e63d34b34 | ||
| 48 | 1 | reports/research_console/.v215_tmp/html_ref_VOC_0413_분석리포트_v5_with_images_010.png | png | 68,785 | 1258x879 | 43926d49a66d2034 | ||
| 49 | 1 | reports/research_console/.v215_tmp/html_ref_VOC_0413_분석리포트_v5_with_images_011.png | png | 69,235 | 1259x879 | b22bb611876741a2 | ||
| 50 | 1 | reports/research_console/.v215_tmp/ref_VOC_0413_분석리포트_v5_with_images_001.png | png | 88,937 | 1258x885 | 084e604b356c81a6 | ||
| 51 | 1 | reports/research_console/.v215_tmp/ref_VOC_0413_분석리포트_v5_with_images_002.png | png | 104,478 | 1258x883 | 43f4052238d8ebd9 | ||
| 52 | 1 | reports/research_console/.v215_tmp/ref_VOC_0413_분석리포트_v5_with_images_003.png | png | 97,741 | 1257x883 | 03bd14e4bab2e528 | ||
| 53 | 1 | reports/research_console/.v215_tmp/ref_VOC_0413_분석리포트_v5_with_images_004.png | png | 86,929 | 1258x883 | 1a9a187637c166ba | ||
| 54 | 1 | reports/research_console/.v215_tmp/ref_VOC_0413_분석리포트_v5_with_images_005.png | png | 105,214 | 1258x884 | 675239ceaf491d7f | ||
| 55 | 1 | reports/research_console/.v215_tmp/ref_VOC_0413_분석리포트_v5_with_images_006.png | png | 99,078 | 1557x887 | a40804f8cde3dcb2 | ||
| 56 | 1 | reports/research_console/.v215_tmp/ref_VOC_0413_분석리포트_v5_with_images_007.png | png | 117,550 | 1783x913 | e3c6250601fda513 | ||
| 57 | 1 | reports/research_console/.v215_tmp/ref_VOC_0413_분석리포트_v5_with_images_008.png | png | 73,332 | 1258x879 | 036d080d37dc6970 | ||
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| 245 | 1 | research_continuity/06_quality_guards_v152/kspon_guard_summary_v152.csv | csv | 111 | 2 | 6 | f947dbce5a53bcc1 | |
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| 262 | 1 | research_continuity/06_quality_guards_v155/kspon_guard_summary_v155.csv | csv | 349 | 1 | 14 | 9b1a1cd34a3a9414 | |
| 263 | 1 | research_continuity/06_quality_guards_v155/kspon_guard_v155.json | json | 1,242 | 1 | 21 | aea9663aa941dfcd | |
| 264 | 1 | research_continuity/06_quality_guards_v155/missing_transcript_resolution_v155.csv | csv | 6,566 | 24 | 8 | c4cbe4de280eff43 | |
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| 266 | 1 | research_continuity/06_quality_guards_v156/kspon_guard_summary_v156.csv | csv | 522 | 1 | 13 | 3aabd497a398d851 | |
| 267 | 1 | research_continuity/06_quality_guards_v156/kspon_guard_v156.json | json | 6,948 | 1 | 34 | 1f7f6c89fc746a6f | |
| 268 | 1 | research_continuity/06_quality_guards_v156/missing_transcript_resolution_v156.csv | csv | 1 | 01ba4719c80b6fe9 | |||
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| 270 | 1 | research_continuity/06_quality_guards_v160/kspon_guard_summary_v160.csv | csv | 522 | 1 | 13 | 3aabd497a398d851 | |
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| 272 | 1 | research_continuity/07_kspon_validation_v155/kspon_hypothesis_discovery_v155.csv | csv | 5,658 | 54 | 6 | 7517803407ed2835 | |
| 273 | 1 | research_continuity/07_kspon_validation_v155/kspon_pcm_txt_pairs_v155.csv | csv | 4,289,819 | 17,622 | 8 | 6b48ccc39c6c8f4b | |
| 274 | 1 | research_continuity/07_kspon_validation_v155/kspon_source_discovery_v155.csv | csv | 97 | 1 | 4 | ae2fef8b3961c4c2 | |
| 275 | 1 | research_continuity/07_kspon_validation_v155/kspon_wer_cer_by_file_v155.csv | csv | 28,779 | 100 | 14 | 0ba91c838d429da4 | |
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| 277 | 1 | research_continuity/07_kspon_validation_v155/stt_txt/ksponspeech_000001.txt | txt | 28 | c7271cc9cd26118f | |||
| 278 | 1 | research_continuity/07_kspon_validation_v155/stt_txt/ksponspeech_000002.txt | txt | 156 | 533b55271bf5416d | |||
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| 284 | 1 | research_continuity/07_kspon_validation_v155/stt_txt/ksponspeech_000008.txt | txt | 7 | 0cfab5cbe7caac82 | |||
| 285 | 1 | research_continuity/07_kspon_validation_v155/stt_txt/ksponspeech_000009.txt | txt | 47 | a62ad3e8091bc6e5 | |||
| 286 | 1 | research_continuity/07_kspon_validation_v155/stt_txt/ksponspeech_000010.txt | txt | 30 | f6cadbc08b48d65a | |||
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| 297 | 1 | research_continuity/07_kspon_validation_v155/stt_txt/ksponspeech_000021.txt | txt | 3 | 08b35eb63efd4aaa | |||
| 298 | 1 | research_continuity/07_kspon_validation_v155/stt_txt/ksponspeech_000022.txt | txt | 99 | 3f018e822cb7e1f3 | |||
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| 300 | 1 | research_continuity/07_kspon_validation_v155/stt_txt/ksponspeech_000024.txt | txt | 77 | 61af1552eef03873 |
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| 301 | 1 | research_continuity/07_kspon_validation_v155/stt_txt/ksponspeech_000025.txt | txt | 39 | 07dd93c6901ad36a | |||
| 302 | 1 | research_continuity/07_kspon_validation_v155/stt_txt/ksponspeech_000026.txt | txt | 24 | 4415cbcf0cc4a9f9 | |||
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| 311 | 1 | research_continuity/07_kspon_validation_v155/stt_txt/ksponspeech_000035.txt | txt | 38 | e762a11ed893447c | |||
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| 327 | 1 | research_continuity/07_kspon_validation_v156/kspon_hypothesis_discovery_v156.csv | csv | 5,658 | 54 | 6 | 294179f19d646618 | |
| 328 | 1 | research_continuity/07_kspon_validation_v156/kspon_pcm_txt_pairs_v156.csv | csv | 4,289,819 | 17,622 | 8 | 6b48ccc39c6c8f4b | |
| 329 | 1 | research_continuity/07_kspon_validation_v156/kspon_source_discovery_v156.csv | csv | 1 | 01ba4719c80b6fe9 | |||
| 330 | 1 | research_continuity/07_kspon_validation_v156/kspon_wer_cer_by_file_v156.csv | csv | 28,779 | 100 | 14 | 0ba91c838d429da4 | |
| 331 | 1 | research_continuity/07_kspon_validation_v156/kspon_wer_cer_summary_v156.csv | csv | 522 | 1 | 13 | 3aabd497a398d851 | |
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| 449 | 1 | research_continuity/24_speaker_actual_diarization_v176/speaker_actual_diarization_status_v176.json | json | 4,362 | 1 | 25 | 4f43c975799e1327 | |
| 450 | 1 | research_continuity/24_speaker_actual_diarization_v181/speaker_actual_diarization_status_v181.json | json | 5,233 | 1 | 35 | 3b575308c04de576 | |
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| 583 | 2 | results/tables/univariate_ols.csv | csv | 516 | 4 | 8 | 4f168b713cef00eb | |
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| 599 | 3 | research_continuity/02_asis_same_method_reproduction/NEEDS_ACTUAL_ASIS_INPUTS.md | md | 811 | 0653b80dc31bd8f2 | |||
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| 603 | 3 | research_continuity/02_asis_same_method_reproduction/README.md | md | 160 | 491607770d35874f | |||
| 604 | 3 | research_continuity/02_asis_same_method_reproduction/runs/20260705_191034/actual_asis_source_schema_v150.csv | csv | 235 | 6 | 5 | 79de861b4596f5ad | |
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| 614 | 3 | research_continuity/02_asis_same_method_reproduction/runs/20260705_203636/actual_same_method_normalization_params_v150.json | json | 819 | 1 | 7 | 5f008a4384aad3ce | |
| 615 | 3 | research_continuity/02_asis_same_method_reproduction/runs/20260705_203636/actual_same_method_reproduction_manifest_v150.json | json | 1,722 | 1 | 16 | f221c73bdef33d5e | |
| 616 | 3 | research_continuity/02_asis_same_method_reproduction/runs/20260705_213459/actual_same_method_normalization_candidate_params_v153.json | json | 692 | 1 | 6 | 9c6e53d3a9924eb7 | |
| 617 | 3 | research_continuity/02_asis_same_method_reproduction/runs/20260705_213459/actual_same_method_reproduction_manifest_v153.json | json | 2,463 | 1 | 17 | b7a0d525221ea6e5 | |
| 618 | 3 | research_continuity/02_asis_same_method_reproduction/runs/20260705_221100/actual_same_method_normalization_candidate_params_v153.json | json | 692 | 1 | 6 | 31ea7e130df37f2e | |
| 619 | 3 | research_continuity/02_asis_same_method_reproduction/runs/20260705_221100/actual_same_method_reproduction_manifest_v153.json | json | 2,463 | 1 | 17 | 333fba1529f3d261 | |
| 620 | 3 | research_continuity/02_asis_same_method_reproduction/runs/20260705_230644/actual_same_method_normalization_candidate_params_v153.json | json | 692 | 1 | 6 | f096cee02263b753 | |
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| 624 | 3 | research_continuity/02_asis_same_method_reproduction/runs/20260706_080318/actual_same_method_normalization_candidate_params_v153.json | json | 663 | 1 | 6 | ae5b206959557e07 | |
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| 627 | 3 | research_continuity/02_asis_same_method_reproduction/runs/20260706_092758/actual_same_method_reproduction_manifest_v153.json | json | 2,401 | 1 | 17 | b2d5d00033f9bf84 | |
| 628 | 3 | research_continuity/02_asis_same_method_reproduction/runs/20260706_095102/actual_same_method_normalization_candidate_params_v153.json | json | 663 | 1 | 6 | 0257c35866a81c02 | |
| 629 | 3 | research_continuity/02_asis_same_method_reproduction/runs/20260706_095102/actual_same_method_reproduction_manifest_v153.json | json | 2,401 | 1 | 17 | ebe6b3d21aae12bf | |
| 630 | 3 | research_continuity/02_asis_same_method_reproduction/runs/20260706_101427/actual_same_method_normalization_candidate_params_v153.json | json | 663 | 1 | 6 | 1e6bb392bfa526c6 | |
| 631 | 3 | research_continuity/02_asis_same_method_reproduction/runs/20260706_101427/actual_same_method_reproduction_manifest_v153.json | json | 2,401 | 1 | 17 | 1e3316c47c190e03 | |
| 632 | 3 | research_continuity/02_asis_same_method_reproduction/runs/20260706_102806/actual_same_method_normalization_candidate_params_v153.json | json | 663 | 1 | 6 | ab69f74840871e15 | |
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| 640 | 3 | research_continuity/02_asis_same_method_reproduction/runs/20260706_120318/actual_same_method_normalization_candidate_params_v153.json | json | 692 | 1 | 6 | a616900bed038f2c | |
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| 647 | 3 | research_continuity/02_asis_same_method_reproduction/runs/20260706_152116/actual_same_method_reproduction_manifest_v153.json | json | 2,463 | 1 | 17 | 0747a06b578827e5 | |
| 648 | 3 | research_continuity/02_asis_same_method_reproduction/runs/20260706_153054/actual_same_method_normalization_candidate_params_v153.json | json | 692 | 1 | 6 | 42b1d19f376d3a84 | |
| 649 | 3 | research_continuity/02_asis_same_method_reproduction/runs/20260706_153054/actual_same_method_reproduction_manifest_v153.json | json | 2,463 | 1 | 17 | 57eb0e1bebdba444 | |
| 650 | 3 | research_continuity/02_asis_same_method_reproduction/runs/20260706_155722/actual_same_method_normalization_candidate_params_v153.json | json | 692 | 1 | 6 | 382ca5d80bb52305 | |
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| 652 | 3 | research_continuity/02_asis_same_method_reproduction/runs/20260706_171123/actual_same_method_normalization_candidate_params_v153.json | json | 692 | 1 | 6 | b6f9366247b0b098 | |
| 653 | 3 | research_continuity/02_asis_same_method_reproduction/runs/20260706_171123/actual_same_method_reproduction_manifest_v153.json | json | 2,463 | 1 | 17 | b6ee9e3acdec1969 | |
| 654 | 3 | research_continuity/02_asis_same_method_reproduction/runs/20260706_182111/actual_same_method_normalization_candidate_params_v153.json | json | 692 | 1 | 6 | b9122df786b52657 | |
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| 670 | 3 | research_continuity/02_asis_same_method_reproduction/runs/20260707_113522/actual_same_method_normalization_candidate_params_v153.json | json | 692 | 1 | 6 | effd0311aa7eac07 | |
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| 672 | 3 | research_continuity/02_asis_same_method_reproduction/same_method_lock_checklist_v149.csv | csv | 1,062 | 8 | 3 | 5b65ba94bb627354 | |
| 673 | 3 | research_continuity/02_asis_same_method_reproduction/same_method_lock_checklist_v150.csv | csv | 1,062 | 8 | 3 | 5b65ba94bb627354 | |
| 674 | 3 | research_continuity/02_asis_same_method_reproduction/same_method_lock_checklist_v152.csv | csv | 370 | 4 | 3 | bcd6b47764acce48 | |
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| 677 | 3 | research_continuity/02_asis_same_method_reproduction/same_method_lock_v149.json | json | 1,229 | 1 | 8 | dea76db7d2ffccaf | |
| 678 | 3 | research_continuity/02_asis_same_method_reproduction/same_method_lock_v150.json | json | 1,210 | 1 | 8 | c7c9699d0f809bb7 | |
| 679 | 3 | research_continuity/02_asis_same_method_reproduction/same_method_lock_v152.json | json | 1,710 | 1 | 12 | 8204d591003173ce | |
| 680 | 3 | research_continuity/02_asis_same_method_reproduction/same_method_lock_v153.json | json | 2,088 | 1 | 14 | 34e734ab1e1947a3 | |
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| 682 | 3 | research_continuity/02_asis_same_method_reproduction/same_method_lock_v155.json | json | 804 | 1 | 6 | fedc49907ec65e64 | |
| 683 | 3 | research_continuity/02_asis_same_method_reproduction/same_method_lock_v156.json | json | 836 | 1 | 6 | 8f7225b18183d8fc | |
| 684 | 3 | research_continuity/02_asis_same_method_reproduction/same_method_reproduction_checklist.csv | csv | 1,935 | 12 | 4 | bd6289c9897c20a9 | |
| 685 | 3 | research_continuity/02_asis_same_method_reproduction/same_method_reproduction_recipe.csv | csv | 478 | 5 | 4 | b58edb6d411fe446 | |
| 686 | 3 | research_continuity/04_three_stage_comparison/asis_vs_actual_reproduction_matched_preview_v153.csv | csv | 120,332 | 200 | 33 | 6d630b0052e8fe01 | |
| 687 | 3 | research_continuity/04_three_stage_comparison/asis_vs_actual_reproduction_matched_preview_v154.csv | csv | 120,332 | 200 | 33 | 6d630b0052e8fe01 | |
| 688 | 3 | research_continuity/04_three_stage_comparison/asis_vs_actual_reproduction_matched_preview_v155.csv | csv | 120,533 | 200 | 33 | 8bc24a104f826eec | |
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| 690 | 3 | research_continuity/04_three_stage_comparison/asis_vs_actual_reproduction_validation_v153.csv | csv | 1,150 | 6 | 12 | 136befce3164806a | |
| 691 | 3 | research_continuity/04_three_stage_comparison/asis_vs_actual_reproduction_validation_v154.csv | csv | 1,150 | 6 | 12 | 136befce3164806a | |
| 692 | 3 | research_continuity/04_three_stage_comparison/asis_vs_actual_reproduction_validation_v155.csv | csv | 1,150 | 6 | 12 | 83df2a59851c9c6b | |
| 693 | 3 | research_continuity/04_three_stage_comparison/asis_vs_actual_reproduction_validation_v156.csv | csv | 1,150 | 6 | 12 | 136befce3164806a | |
| 694 | 3 | research_continuity/04_three_stage_comparison/asis_vs_reproduction_needs_reproduction_v149.json | json | 145 | 1 | 2 | 2522d0735e2628e5 | |
| 695 | 3 | research_continuity/04_three_stage_comparison/latest_asis_vs_actual_reproduction_matched_preview_v150.csv | csv | 90,595 | 200 | 26 | 3771485559e73a74 | |
| 696 | 3 | research_continuity/04_three_stage_comparison/latest_asis_vs_actual_reproduction_validation_manifest_v150.json | json | 1,125 | 1 | 12 | 88c438b59b6e178a | |
| 697 | 3 | research_continuity/04_three_stage_comparison/latest_asis_vs_actual_reproduction_validation_manifest_v152.json | json | 1,077 | 1 | 13 | c7db3b59914d4813 | |
| 698 | 3 | research_continuity/04_three_stage_comparison/latest_asis_vs_actual_reproduction_validation_manifest_v153.json | json | 1,125 | 1 | 12 | 88c438b59b6e178a | |
| 699 | 3 | research_continuity/04_three_stage_comparison/latest_asis_vs_actual_reproduction_validation_manifest_v154.json | json | 1,260 | 1 | 13 | bb60673a63deb827 | |
| 700 | 3 | research_continuity/04_three_stage_comparison/latest_asis_vs_actual_reproduction_validation_manifest_v155.json | json | 1,283 | 1 | 13 | 88dcc0e0918cecfa |
| 번호 | 장 | 파일 | 형식 | 크기(bytes) | 행 | 열/키 | 이미지크기 | SHA256(앞16) |
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| 701 | 3 | research_continuity/04_three_stage_comparison/latest_asis_vs_actual_reproduction_validation_manifest_v156.json | json | 1,260 | 1 | 13 | bb60673a63deb827 | |
| 702 | 3 | research_continuity/04_three_stage_comparison/latest_asis_vs_actual_reproduction_validation_v150.csv | csv | 1,150 | 6 | 12 | 136befce3164806a | |
| 703 | 3 | research_continuity/04_three_stage_comparison/professor_actual_reproduction_report_v149.md | md | 5,604 | 55c95c3fb1ad667f | |||
| 704 | 3 | research_continuity/04_three_stage_comparison/professor_actual_reproduction_report_v150.md | md | 7,525 | 3f4a73b81174caae | |||
| 705 | 3 | research_continuity/04_three_stage_comparison/professor_actual_reproduction_report_v152.md | md | 5,851 | a4886465a4814dc8 | |||
| 706 | 3 | research_continuity/04_three_stage_comparison/professor_actual_reproduction_report_v153.md | md | 7,525 | 3f4a73b81174caae | |||
| 707 | 3 | research_continuity/04_three_stage_comparison/professor_actual_reproduction_report_v154.md | md | 1,520 | 85ce282de1447d9d | |||
| 708 | 3 | research_continuity/04_three_stage_comparison/professor_actual_reproduction_report_v155.md | md | 1,177 | cfa93caa0c332339 | |||
| 709 | 3 | research_continuity/04_three_stage_comparison/professor_actual_reproduction_report_v156.md | md | 1,150 | 61f25570ee31e42b | |||
| 710 | 3 | research_continuity/04_three_stage_comparison/runs/20260705_191034/asis_vs_actual_reproduction_matched_preview_v150.csv | csv | 794 | 3 | 19 | 227f5ca91ed2e12f | |
| 711 | 3 | research_continuity/04_three_stage_comparison/runs/20260705_191034/asis_vs_actual_reproduction_validation_manifest_v150.json | json | 953 | 1 | 11 | 53f3d0e88d0e7c1d | |
| 712 | 3 | research_continuity/04_three_stage_comparison/runs/20260705_191034/asis_vs_actual_reproduction_validation_v150.csv | csv | 458 | 5 | 10 | 377670f7a6ee79bf | |
| 713 | 3 | research_continuity/04_three_stage_comparison/runs/20260705_191056/asis_vs_actual_reproduction_matched_preview_v150.csv | csv | 794 | 3 | 19 | c93f1c831df82dc8 | |
| 714 | 3 | research_continuity/04_three_stage_comparison/runs/20260705_191056/asis_vs_actual_reproduction_validation_manifest_v150.json | json | 953 | 1 | 11 | 37f76f3e544ffd91 | |
| 715 | 3 | research_continuity/04_three_stage_comparison/runs/20260705_191056/asis_vs_actual_reproduction_validation_v150.csv | csv | 458 | 5 | 10 | 377670f7a6ee79bf | |
| 716 | 3 | research_continuity/04_three_stage_comparison/runs/20260705_194459/asis_vs_actual_reproduction_matched_preview_v150.csv | csv | 794 | 3 | 19 | 5f332b8adbc35d11 | |
| 717 | 3 | research_continuity/04_three_stage_comparison/runs/20260705_194459/asis_vs_actual_reproduction_validation_manifest_v150.json | json | 953 | 1 | 11 | 41bc3d72cc0c7834 | |
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| 719 | 3 | research_continuity/04_three_stage_comparison/runs/20260705_203636/asis_vs_actual_reproduction_matched_preview_v150.csv | csv | 90,595 | 200 | 26 | 3771485559e73a74 | |
| 720 | 3 | research_continuity/04_three_stage_comparison/runs/20260705_203636/asis_vs_actual_reproduction_validation_manifest_v150.json | json | 1,011 | 1 | 11 | 913ed9565bdb320b | |
| 721 | 3 | research_continuity/04_three_stage_comparison/runs/20260705_203636/asis_vs_actual_reproduction_validation_v150.csv | csv | 809 | 5 | 10 | 59b4d693dfad0bb4 | |
| 722 | 3 | results/manuscript_text/aiis_final_diff_analysis_ko.md | md | 2,588 | f502dd80ccaf812d | |||
| 723 | 3 | results/manuscript_text/monitoring/monitoring_dashboard_n1000_ko.md | md | 469 | 9906a577a2283c26 | |||
| 724 | 3 | results/manuscript_text/repro/artifact_lineage_ko.md | md | 1,350 | 2afba80d11677842 | |||
| 725 | 3 | results/manuscript_text/repro/auto_execution_plan_ko.md | md | 261 | 84bcf4eca4cccc65 | |||
| 726 | 3 | results/manuscript_text/repro/final_submission_board_ko.md | md | 547 | 69e5536a68601e52 | |||
| 727 | 3 | results/runtime/monitoring_dashboard_n1000.json | json | 972 | 1 | 9 | dff754103c088706 | |
| 728 | 3 | results/runtime/resume/stt_checkpoint_voc_n1000_medium.json | json | 573 | 1 | 15 | 2940f6b87fbf63b7 | |
| 729 | 3 | results/runtime/resume/stt_checkpoint_voc_n1000_small.json | json | 525 | 1 | 15 | 938622d907c74f89 | |
| 730 | 3 | results/tables/aiis_final_diff_actions.csv | csv | 328 | 2 | 4 | 16e2a8b81d65b3f4 | |
| 731 | 3 | results/tables/aiis_final_diff_metrics.csv | csv | 1,400 | 10 | 8 | c63e246f82069a04 | |
| 732 | 3 | results/tables/aiis_final_diff_root_causes.csv | csv | 1,432 | 5 | 7 | 235923c0d6d9a397 | |
| 733 | 3 | results/tables/aiis_key_metric_compare.csv | csv | 1,239 | 6 | 8 | 8b0e21aa8898a48a | |
| 734 | 3 | results/tables/aiis_recommended_actions.csv | csv | 423 | 1 | 7 | 7da73640d0b8732b | |
| 735 | 3 | results/tables/aiis_root_causes.csv | csv | 516 | 1 | 10 | 17abe2b01318d8cb | |
| 736 | 3 | results/tables/aiis_tracking_metrics.csv | csv | 576 | 4 | 5 | a45632bcabcb49cc | |
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