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DIEZ SÁNCHEZ, M. BURGET, L. LANDINI, F. ČERNOCKÝ, J.
Original Title
Analysis of Speaker Diarization based on Bayesian HMM with Eigenvoice Priors
Type
journal article in Web of Science
Language
English
Original Abstract
In our previous work, we introduced our Bayesian Hidden Markov Model with eigenvoice priors, which has been recently recognized as the state-of-the-art model for Speaker Diarization. In this paper we present a more complete analysis of the Diarization system. The inference of the model is fully described and derivations of all update formulas are provided for a complete understanding of the algorithm. An extensive analysis on the effect, sensitivity and interactions of all model parameters is provided, which might be used as a guide for their optimal setting. The newly introduced speaker regularization coefficient allows us to control the number of speakers inferred in an utterance. A naive speaker model merging strategy is also presented, which allows to drive the variational inference out of local optima. Experiments for the different diarization scenarios are presented on CALLHOME and DIHARD datasets.
Keywords
Hidden Markov Models, Bayes methods, Task analysis, Probabilistic logic, Training, Speech processing, Complexity theory
Authors
DIEZ SÁNCHEZ, M.; BURGET, L.; LANDINI, F.; ČERNOCKÝ, J.
Released
1. 12. 2020
ISBN
2329-9290
Periodical
IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH AND LANGUAGE PROCESSING
Year of study
28
Number
1
State
United States of America
Pages from
355
Pages to
368
Pages count
14
URL
https://ieeexplore.ieee.org/document/8910412
BibTex
@article{BUT161472, author="Mireia {Diez Sánchez} and Lukáš {Burget} and Federico Nicolás {Landini} and Jan {Černocký}", title="Analysis of Speaker Diarization based on Bayesian HMM with Eigenvoice Priors", journal="IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH AND LANGUAGE PROCESSING", year="2020", volume="28", number="1", pages="355--368", doi="10.1109/TASLP.2019.2955293", issn="2329-9290", url="https://ieeexplore.ieee.org/document/8910412" }