2022
Auxiliary Loss of Transformer with Residual Connection for End-to-End Speaker Diarization
ICASSP 2022accepted
End-to-end neural diarization (EEND) with self-attention directly predicts speaker labels from inputs and enables the handling of overlapped speech. Although the EEND outperforms clustering-based speaker diarization (SD), it cannot be further improved by simply increasing the number of encoder block…