Detail publikace

Bayesian HMM based x-vector clustering for Speaker Diarization

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

Originální název

Bayesian HMM based x-vector clustering for Speaker Diarization

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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 embeddings). The presented results show that this clustering algorithm provides significant improvements in diarization performance as compared to the previously used Agglomerative Hierarchical Clustering. The output of this system can be further employed as an initialization for a second stage VB diarization system, using frame-wise MFCC features as input, to obtain optimal results.

Klíčová slova

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

Autoři

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

Vydáno

15. 9. 2019

Nakladatel

International Speech Communication Association

Místo

Graz

ISSN

1990-9772

Periodikum

Proceedings of Interspeech

Ročník

2019

Číslo

9

Stát

Francouzská republika

Strany od

346

Strany do

350

Strany počet

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"
}