Přístupnostní navigace
E-přihláška
Vyhledávání Vyhledat Zavřít
Detail publikace
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" }