# Joint speaker diarisation and tracking in switching state-space model

arXiv: https://arxiv.org/abs/2109.11140 · HTML: https://mlsanity.com/paper/2109.11140/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 41 references that are in the map.
- First posted on arXiv 23 Sep 2021.

## Builds on (most cited first, 9 of 9)

- [Auto-Tuning Spectral Clustering for Speaker Diarization Using Normalized Maximum Eigengap](https://mlsanity.com/paper/2003.02405/) (2020)
- [Advances in Online Audio-Visual Meeting Transcription](https://mlsanity.com/paper/1912.04979/) (2019)
- [Encoder-Decoder Based Attractors for End-to-End Neural Diarization](https://mlsanity.com/paper/2106.10654/) (2021)
- [The LOCATA Challenge: Acoustic Source Localization and Tracking](https://mlsanity.com/paper/1909.01008/) (2019)
- [Towards Neural Diarization for Unlimited Numbers of Speakers Using Global and Local Attractors](https://mlsanity.com/paper/2107.01545/) (2021)
- [The continuous categorical: a novel simplex-valued exponential family](https://mlsanity.com/paper/2002.08563/) (2020)
- [BUT System for the Second DIHARD Speech Diarization Challenge](https://mlsanity.com/paper/2002.11356/) (2020)
- [ResNeXt and Res2Net Structures for Speaker Verification](https://mlsanity.com/paper/2007.02480/) (2020)
- [Localization and Tracking of an Acoustic Source using a Diagonal Unloading Beamforming and a Kalman Filter](https://mlsanity.com/paper/1812.01521/) (2018)

## Abstract (arXiv)

Speakers may move around while diarisation is being performed. When a microphone array is used, the instantaneous locations of where the sounds originated from can be estimated, and previous investigations have shown that such information can be complementary to speaker embeddings in the diarisation task. However, these approaches often assume that speakers are fairly stationary throughout a meeting. This paper relaxes this assumption, by proposing to explicitly track the movements of speakers while jointly performing diarisation within a unified model. A state-space model is proposed, where the hidden state expresses the identity of the current active speaker and the predicted locations of all speakers. The model is implemented as a particle filter. Experiments on a Microsoft rich meeting transcription task show that the proposed joint location tracking and diarisation approach is able to perform comparably with other methods that use location information.