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.
@article{arxiv.2403.14268,
title = {Speech-Aware Neural Diarization with Encoder-Decoder Attractor Guided by Attention Constraints},
author = {PeiYing Lee and HauYun Guo and Berlin Chen},
journal= {arXiv preprint arXiv:2403.14268},
year = {2024}
}
Comments
Accepted to The 28th International Conference on Technologies and Applications of Artificial Intelligence (TAAI), in Chinese language