# Analysis of the BUT Diarization System for VoxConverse Challenge

arXiv: https://arxiv.org/abs/2010.11718 · HTML: https://mlsanity.com/paper/2010.11718/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 11 of 11)

- [AVA-AVD: Audio-Visual Speaker Diarization in the Wild](https://mlsanity.com/paper/2111.14448/) (2021)
- [The VoxCeleb Speaker Recognition Challenge: A Retrospective](https://mlsanity.com/paper/2408.14886/) (2024)
- [Supervised Hierarchical Clustering using Graph Neural Networks for Speaker Diarization](https://mlsanity.com/paper/2302.12716/) (2023)
- [Adapting Speaker Embeddings for Speaker Diarisation](https://mlsanity.com/paper/2104.02879/) (2021)
- [Royalflush Speaker Diarization System for ICASSP 2022 Multi-channel Multi-party Meeting Transcription Challenge](https://mlsanity.com/paper/2202.04814/) (2022)
- [End-to-End Integration of Speech Separation and Voice Activity Detection for Low-Latency Diarization of Telephone Conversations](https://mlsanity.com/paper/2303.12002/) (2023)
- [Online speaker diarization of meetings guided by speech separation](https://mlsanity.com/paper/2402.00067/) (2024)
- [Scalable Data Annotation Pipeline for High-Quality Large Speech Datasets Development](https://mlsanity.com/paper/2109.01164/) (2021)
- [ASoBO: Attentive Beamformer Selection for Distant Speaker Diarization in Meetings](https://mlsanity.com/paper/2406.03251/) (2024)
- [The Newsbridge -Telecom SudParis VoxCeleb Speaker Recognition Challenge 2022 System Description](https://mlsanity.com/paper/2301.07491/) (2023)
- [O-EENC-SD: Efficient Online End-to-End Neural Clustering for Speaker Diarization](https://mlsanity.com/paper/2512.15229/) (2025)

## Abstract (arXiv)

This paper describes the system developed by the BUT team for the fourth track of the VoxCeleb Speaker Recognition Challenge, focusing on diarization on the VoxConverse dataset. The system consists of signal pre-processing, voice activity detection, speaker embedding extraction, an initial agglomerative hierarchical clustering followed by diarization using a Bayesian hidden Markov model, a reclustering step based on per-speaker global embeddings and overlapped speech detection and handling. We provide comparisons for each of the steps and share the implementation of the most relevant modules of our system. Our system scored second in the challenge in terms of the primary metric (diarization error rate) and first according to the secondary metric (Jaccard error rate).