# Spatio-spectral diarization of meetings by combining TDOA-based segmentation and speaker embedding-based clustering

arXiv: https://arxiv.org/abs/2506.16228 · HTML: https://mlsanity.com/paper/2506.16228/

## Key facts

- Cited by 3 papers in the map (data through 19 Oct 2026).
- First posted on arXiv 19 Jun 2025.

## Built on by (most cited first, 3 of 3)

- [Spatially Aware Self-Supervised Models for Multi-Channel Neural Speaker Diarization](https://mlsanity.com/paper/2510.14551/) (2025)
- [On the Role of Spatial Features in Foundation-Model-Based Speaker Diarization](https://mlsanity.com/paper/2601.02231/) (2026)
- [Investigating the Integration of Spatial Information in Foundation-Model-Based Speaker Diarization](https://mlsanity.com/paper/2607.12647/) (2026)

## Abstract (arXiv)

We propose a spatio-spectral, combined model-based and data-driven diarization pipeline consisting of TDOA-based segmentation followed by embedding-based clustering. The proposed system requires neither access to multi-channel training data nor prior knowledge about the number or placement of microphones. It works for both a compact microphone array and distributed microphones, with minor adjustments. Due to its superior handling of overlapping speech during segmentation, the proposed pipeline significantly outperforms the single-channel pyannote approach, both in a scenario with a compact microphone array and in a setup with distributed microphones. Additionally, we show that, unlike fully spatial diarization pipelines, the proposed system can correctly track speakers when they change positions.