Kernekoncepter
Attractors in end-to-end neural diarization may not require encoding speaker characteristic information.
Statistik
EEND-EDA는 두 가지 분기로 구성됨: 프레임 임베딩 분기와 어트랙터 분기.
Attractors의 존재 확률을 추정하기 위해 sigmoid 함수 사용.
Attractors 손실은 이진 교차 엔트로피 함수로 정의됨.
Citater
"Attractors might not need to encode specific speaker identities but rather enough information to distinguish them in a given conversation."
"Findings could lead to more effective strategies for training speaker diarization systems."