# Simultaneous Diarization and Separation of Meetings through the Integration of Statistical Mixture Models

arXiv: https://arxiv.org/abs/2410.21455 · HTML: https://mlsanity.com/paper/2410.21455/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2024.

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

- [LibriVAD: A Scalable Open Dataset with Deep Learning Benchmarks for Voice Activity Detection](https://mlsanity.com/paper/2512.17281/) (2025)

## Abstract (arXiv)

We propose an approach for simultaneous diarization and separation of meeting data. It consists of a complex Angular Central Gaussian Mixture Model (cACGMM) for speech source separation, and a von-Mises-Fisher Mixture Model (VMFMM) for diarization in a joint statistical framework. Through the integration, both spatial and spectral information are exploited for diarization and separation. We also develop a method for counting the number of active speakers in a segment of a meeting to support block-wise processing. While the total number of speakers in a meeting may be known, it is usually not known on a per-segment level. With the proposed speaker counting, joint diarization and source separation can be done segment-by-segment, and the permutation problem across segments is solved, thus allowing for block-online processing in the future. Experimental results on the LibriCSS meeting corpus show that the integrated approach outperforms a cascaded approach of diarization and speech enhancement in terms of WER, both on a per-segment and on a per-meeting level.