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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 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.
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" }