Result Details
Optimizing Bayesian Hmm Based X-Vector Clustering for the Second Dihard Speech Diarization Challenge
Burget Lukáš, doc. Ing., Ph.D., DCGM (FIT)
Landini Federico Nicolás, Ph.D., DCGM (FIT)
Wang Shuai
Černocký Jan, prof. Dr. Ing., DCGM (FIT)
This paper presents an analysis of our diarization systemwinning the second DIHARD speech diarization challenge,track 1. This system is based on clustering x-vector speakerembeddings extracted every 0.25s from short segments of theinput recording. In this paper, we focus on the two x-vectorclustering methods employed, namely Agglomerative HierarchicalClustering followed by a clustering based on BayesianHidden Markov Model (BHMM). Even though the systemsubmitted to the challenge had further post-processing steps,we will show that using this BHMM solely is enough toachieve the best performance in the challenge. The analysiswill show improvements achieved by optimizing individualprocessing steps, including a simple procedure to effectivelyperform "domain adaptation" by Probabilistic LinearDiscriminant Analysis model interpolation. All experimentsare performed in the DIHARD II evaluation framework.
Speaker Diarization, Variational Bayes, HMM, x-vector, DIHARD
@inproceedings{BUT163963,
author="Mireia {Diez Sánchez} and Lukáš {Burget} and Federico Nicolás {Landini} and Shuai {Wang} and Jan {Černocký}",
title="Optimizing Bayesian Hmm Based X-Vector Clustering for the Second Dihard Speech Diarization Challenge",
booktitle="ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
year="2020",
pages="6519--6523",
publisher="IEEE Signal Processing Society",
address="Barcelona",
doi="10.1109/ICASSP40776.2020.9053982",
isbn="978-1-5090-6631-5",
url="https://ieeexplore.ieee.org/document/9053982"
}Moderní metody zpracování, analýzy a zobrazování multimediálních a 3D dat, BUT, Vnitřní projekty VUT, FIT-S-20-6460, start: 2020-03-01, end: 2023-02-28, completed