But System for the Second Dihard Speech Diarization Challenge

This paper describes the winning systems developed by the BUT team for the four tracks of the Second DIHARD Speech Diarization Challenge, with source code available

Optimizing Bayesian HMM based x-vector clustering for the second DIHARD speech diarization challenge

This paper presents an analysis of our diarization system winning the second DIHARD speech diarization challenge, track 1. This system is based on clustering x-vector speaker embeddings extracted every 0.25s from short segments of the input …

Bayesian HMM Based x-Vector Clustering for Speaker Diarization

This paper presents a simplified version of the previously proposed diarization algorithm based on Bayesian Hidden Markov Models, which uses Variational Bayesian inference for very fast and robust clustering of x-vector (neural network based speaker …