1. Mamba-based Segmentation Model for Speaker Diarization
- Author
-
Plaquet, Alexis, Tawara, Naohiro, Delcroix, Marc, Horiguchi, Shota, Ando, Atsushi, and Araki, Shoko
- Subjects
Computer Science - Sound ,Electrical Engineering and Systems Science - Audio and Speech Processing - Abstract
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., Comment: 5 pages, 4 figures. Submitted to ICASSP 2025. Code at https://github.com/nttcslab-sp/mamba-diarization
- Published
- 2024