Speech-Aware Neural Diarization with Encoder-Decoder Attractor Guided by Attention Constraints
<|reference_start|>Speech-Aware Neural Diarization with Encoder-Decoder Attractor Guided by Attention Constraints: 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.<|reference_end|>
@article{lee2024speech-aware,
    title={Speech-Aware Neural Diarization with Encoder-Decoder Attractor Guided by
  Attention Constraints},
    author={PeiYing Lee, HauYun Guo, Berlin Chen},
    journal={arXiv preprint arXiv:2403.14268},
    year={2024},
    number={TAAI2023-Domestic-131},
    archivePrefix={arXiv},
    eprint={2403.14268},
    primaryClass={eess.AS cs.SD}
}