Mamba-based Segmentation Model for Speaker Diarization
<|reference_start|>Mamba-based Segmentation Model for Speaker Diarization: Mamba is a newly proposed architecture which behaves like a recurrent neural network (RNN) with attention-like capabilities. These properties are promising for speaker diarization, as attention-based models have unsuitable memory requirements for long-form audio, and traditional RNN capabilities are too limited. In this paper, we propose to assess the potential of Mamba for diarization by comparing the state-of-the-art neural segmentation of the pyannote pipeline with our proposed Mamba-based variant. Mamba's stronger processing capabilities allow usage of longer local windows, which significantly improve diarization quality by making the speaker embedding extraction more reliable. We find Mamba to be a superior alternative to both traditional RNN and the tested attention-based model. Our proposed Mamba-based system achieves state-of-the-art performance on three widely used diarization datasets.<|reference_end|>
@article{plaquet2024mamba-based,
    title={Mamba-based Segmentation Model for Speaker Diarization},
    author={Alexis Plaquet, Naohiro Tawara, Marc Delcroix, Shota Horiguchi,
  Atsushi Ando, Shoko Araki},
    journal={arXiv preprint arXiv:2410.06459},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.06459},
    primaryClass={cs.SD eess.AS}
}