  End-to-End Neural Diarization with Encoder-Decoder based Attractor (EEND-EDA)
is an end-to-end neural model for automatic speaker segmentation and labeling.
It achieves the capability to handle flexible number of speakers by estimating
the number of attractors. EEND-EDA, however, struggles to accurately capture
local speaker dynamics. This work proposes an auxiliary loss that aims to guide
the Transformer encoders at the lower layer of EEND-EDA model to enhance the
effect of self-attention modules using speaker activity information. The
results evaluated on public dataset Mini LibriSpeech, demonstrates the
effectiveness of the work, reducing Diarization Error Rate from 30.95% to
28.17%. We will release the source code on GitHub to allow further research and
reproducibility.
