Joon-Young Yang

Improving Transformer-Based End-to-End Speaker Diarization by Assigning Auxiliary Losses to Attention Heads

Transformer-based end-to-end neural speaker diarization (EEND) models utilize the multi-head self-attention (SA) mechanism to enable accurate speaker label prediction in overlapped speech regions. In this study, to enhance the training effectiveness of SA-EEND models, we propose the use of auxiliary…