Publication detail

Bayesian HMM clustering of x-vector sequences (VBx) in speaker diarization: Theory, implementation and analysis on standard tasks

LANDINI, F. PROFANT, J. DIEZ SÁNCHEZ, M. BURGET, L.

Original Title

Bayesian HMM clustering of x-vector sequences (VBx) in speaker diarization: Theory, implementation and analysis on standard tasks

Type

journal article in Web of Science

Language

English

Original Abstract

The recently proposed VBx diarization method uses a Bayesian hidden Markov model to find speaker clusters in a sequence of x-vectors. In this work we perform an extensive comparison of performance of the VBx diarization with other approaches in the literature and we show that VBx achieves superior performance on three of the most popular datasets for evaluating diarization: CALLHOME, AMI and DIHARD II datasets. Further, we present for the first time the derivation and update formulae for the VBx model, focusing on the efficiency and simplicity of this model as compared to the previous and more complex BHMM model working on frame-by-frame standard Cepstral features. Together with this publication, we release the recipe for training the x-vector extractors used in our experiments on both wide and narrowband data, and the VBx recipes that attain state-of-the-art performance on all three datasets. Besides, we point out the lack of a standardized evaluation protocol for AMI dataset and we propose a new protocol for both Beamformed and Mix-Headset audios based on the official AMI partitions and transcriptions.

Keywords

Speaker diarization, Variational Bayes, HMM, x-vector, AMI

Authors

LANDINI, F.; PROFANT, J.; DIEZ SÁNCHEZ, M.; BURGET, L.

Released

1. 1. 2022

ISBN

0885-2308

Periodical

COMPUTER SPEECH AND LANGUAGE

Year of study

71

Number

101254

State

United Kingdom of Great Britain and Northern Ireland

Pages from

1

Pages to

16

Pages count

16

URL

https://www.sciencedirect.com/science/article/pii/S0885230821000619

BibTex

@article{BUT175852,
  author="Federico Nicolás {Landini} and Ján {Profant} and Mireia {Diez Sánchez} and Lukáš {Burget}",
  title="Bayesian HMM clustering of x-vector sequences (VBx) in speaker diarization: Theory, implementation and analysis on standard tasks",
  journal="COMPUTER SPEECH AND LANGUAGE",
  year="2022",
  volume="71",
  number="101254",
  pages="1--16",
  doi="10.1016/j.csl.2021.101254",
  issn="0885-2308",
  url="https://www.sciencedirect.com/science/article/pii/S0885230821000619"
}