# BUT System for the Second DIHARD Speech Diarization Challenge

arXiv: https://arxiv.org/abs/2002.11356 · HTML: https://mlsanity.com/paper/2002.11356/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2020.

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

- [Towards Neural Diarization for Unlimited Numbers of Speakers Using Global and Local Attractors](https://mlsanity.com/paper/2107.01545/) (2021)
- [A Real-time Speaker Diarization System Based on Spatial Spectrum](https://mlsanity.com/paper/2107.09321/) (2021)
- [The Hitachi-JHU DIHARD III System: Competitive End-to-End Neural Diarization and X-Vector Clustering Systems Combined by DOVER-Lap](https://mlsanity.com/paper/2102.01363/) (2021)
- [Online Neural Diarization of Unlimited Numbers of Speakers Using Global and Local Attractors](https://mlsanity.com/paper/2206.02432/) (2022)
- [USTC-NELSLIP System Description for DIHARD-III Challenge](https://mlsanity.com/paper/2103.10661/) (2021)
- [Supervised Hierarchical Clustering using Graph Neural Networks for Speaker Diarization](https://mlsanity.com/paper/2302.12716/) (2023)
- [Joint speech and overlap detection: a benchmark over multiple audio setup and speech domains](https://mlsanity.com/paper/2307.13012/) (2023)
- [Speaker Diarization using Two-pass Leave-One-Out Gaussian PLDA Clustering of DNN Embeddings](https://mlsanity.com/paper/2104.02469/) (2021)
- [Diarisation using location tracking with agglomerative clustering](https://mlsanity.com/paper/2109.10598/) (2021)
- [Joint speaker diarisation and tracking in switching state-space model](https://mlsanity.com/paper/2109.11140/) (2021)
- [Advancing the dimensionality reduction of speaker embeddings for speaker diarisation: disentangling noise and informing speech activity](https://mlsanity.com/paper/2110.03380/) (2021)

## Abstract (arXiv)

This paper describes the winning systems developed by the BUT team for the four tracks of the Second DIHARD Speech Diarization Challenge. For tracks 1 and 2 the systems were mainly based on performing agglomerative hierarchical clustering (AHC) of x-vectors, followed by another x-vector clustering based on Bayes hidden Markov model and variational Bayes inference. We provide a comparison of the improvement given by each step and share the implementation of the core of the system. For tracks 3 and 4 with recordings from the Fifth CHiME Challenge, we explored different approaches for doing multi-channel diarization and our best performance was obtained when applying AHC on the fusion of per channel probabilistic linear discriminant analysis scores.