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

arXiv: https://arxiv.org/abs/2608.28661 · HTML: https://mlsanity.com/paper/2608.28661/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2026.

## Built on by (most cited first, 1 of 1)

- [Neural Multichannel Distant Speaker Diarization with Heavy-tailed Source Separation Model](https://mlsanity.com/paper/2609.12154/) (2026)

## Abstract (arXiv)

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.