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arXiv · 2608.28661

Neural Multichannel Distant Speaker Diarization and Source Separation with Beta Speaker Activity Prior

Sicheng Mao·Mathieu Fontaine·Anthony Larcher·Roland Badeau
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Abstract

Distant speaker diarization remains challenging due to adverse acoustic conditions, varying numbers of speakers and overlapping speech. While data-driven approaches have shown strong performance, model-driven methods offer a compelling alternative by leveraging spatial information from multichannel recordings. This paper is motivated to propose a Bayesian diarization model for a model-driven method called neural FCASA to enhance its robustness. Specifically, we propose a beta prior over speaker activity and hence a variational lower bound objective that can be seen as a regularized continuous speaker activity score in place of the original cross-entropy loss to train the diarization model. Our experiments show significant improvements in terms of Diarization Error Rate by at least 3% (16% relatively) and Jaccard Error Rate by at least 4% (20% relatively) on the AMI dataset compared to the baseline.

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Sicheng Mao, Mathieu Fontaine, Anthony Larcher, Roland Badeau. 2026-08-21. Neural Multichannel Distant Speaker Diarization and Source Separation with Beta Speaker Activity Prior. https://arxiv.org/abs/2608.28661

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