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arXiv · 2403.14268

Speech-Aware Neural Diarization with Encoder-Decoder Attractor Guided by Attention Constraints

PeiYing Lee·HauYun Guo·Berlin Chen
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Abstract

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.

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PeiYing Lee, HauYun Guo, Berlin Chen. 2024-03-21. Speech-Aware Neural Diarization with Encoder-Decoder Attractor Guided by Attention Constraints. https://arxiv.org/abs/2403.14268

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