Detail publikace

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.

Originální název

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

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

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

Autoři

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

Vydáno

1. 1. 2022

ISSN

0885-2308

Periodikum

COMPUTER SPEECH AND LANGUAGE

Ročník

71

Číslo

101254

Stát

Spojené království Velké Británie a Severního Irska

Strany od

1

Strany do

16

Strany počet

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"
}