REWIND Dataset: Privacy-preserving Speaking Status Segmentation from Multimodal Body Movement Signals in the Wild
핵심 개념
Recognizing speaking in humans using machine learning models trained on video and wearable sensor data.
초록
- Introduces the REWIND dataset for speaking status segmentation.
- Challenges of obtaining individual voice recordings in mingling scenarios.
- Baselines for no-audio speaking status segmentation from video, body acceleration, and body pose tracks.
- Importance of high-quality audio recordings for cross-modality studies.
- Implications for social signal processing and computational social science.
요약 맞춤 설정
AI로 다시 쓰기
인용 생성
소스 번역
다른 언어로
마인드맵 생성
소스 콘텐츠 기반
소스 방문
arxiv.org
REWIND Dataset
통계
"High-quality speaking status signals have been obtained from personal head-mounted and directional microphones in seated meetings."
"Acceleration readings were obtained from wearable devices in a badge-like form factor worn by data subjects on the chest."
"Video recordings include top-down and side-elevated views."
인용구
"Recognizing speaking in humans is a central task towards understanding social interactions."
"Machine learning models trained on video and wearable sensor data make it possible to recognize speech by detecting its related gestures in an unobtrusive, privacy-preserving way."
더 깊은 질문
질문 1
REWIND 데이터셋은 말하는 상태 이외의 다른 사회적 신호를 연구하는 데 어떻게 활용될 수 있습니까?
Answer 1 here
질문 2
혼잡한 환경에서 말하는 상태 감지에 대한 자세한 분석의 한계는 무엇입니까?
Answer 2 here
질문 3
REWIND 데이터셋에서 고품질 오디오 녹음이 주석의 신뢰성과 모델 성능에 미치는 영향은 무엇입니까?
Answer 3 here
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