Publication detail

Bayesian HMM based x-vector clustering for Speaker Diarization

DIEZ SÁNCHEZ, M. BURGET, L. WANG, S. ROHDIN, J. ČERNOCKÝ, J.

Original Title

Bayesian HMM based x-vector clustering for Speaker Diarization

Type

conference paper

Language

English

Original Abstract

This paper presents a simplified version of the previously proposeddiarization algorithm based on Bayesian Hidden MarkovModels, which uses Variational Bayesian inference for very fastand robust clustering of x-vector (neural network based speakerembeddings). The presented results show that this clusteringalgorithm provides significant improvements in diarization performanceas compared to the previously used AgglomerativeHierarchical Clustering. The output of this system can be furtheremployed as an initialization for a second stage VB diarizationsystem, using frame-wise MFCC features as input, to obtainoptimal results.

Keywords

Speaker Diarization, Variational Bayes, HMM,x-vector, DIHARD

Authors

DIEZ SÁNCHEZ, M.; BURGET, L.; WANG, S.; ROHDIN, J.; ČERNOCKÝ, J.

Released

15. 9. 2019

Publisher

International Speech Communication Association

Location

Graz

ISBN

1990-9772

Periodical

Proceedings of Interspeech

Year of study

2019

Number

9

State

French Republic

Pages from

346

Pages to

350

Pages count

5

URL

https://www.isca-speech.org/archive/Interspeech_2019/pdfs/2813.pdf

BibTex

@inproceedings{BUT159992,
  author="Mireia {Diez Sánchez} and Lukáš {Burget} and Shuai {Wang} and Johan Andréas {Rohdin} and Jan {Černocký}",
  title="Bayesian HMM based x-vector clustering for Speaker Diarization",
  booktitle="Proceedings of Interspeech",
  year="2019",
  journal="Proceedings of Interspeech",
  volume="2019",
  number="9",
  pages="346--350",
  publisher="International Speech Communication Association",
  address="Graz",
  doi="10.21437/Interspeech.2019-2813",
  issn="1990-9772",
  url="https://www.isca-speech.org/archive/Interspeech_2019/pdfs/2813.pdf"
}

Documents

diez_is2019_192813.pdf