Publication Details
Speaker Diarization based on Bayesian HMM with Eigenvoice Priors
Burget Lukáš, doc. Ing., Ph.D. (DCGM)
Matějka Pavel, Ing., Ph.D. (DCGM)
Speaker diarization, speaker recognition
Nowadays, most speaker diarization methods address the task in two steps: segmentation of the input conversation into (preferably) speaker homogeneous segments, and clustering. Generally, different models and techniques are used for the two steps. In this paper we present a very elegant approach where a straightforward and efficient Variational Bayes (VB) inference in a single probabilistic model addresses the complete SD problem. Our model is a Bayesian Hidden Markov Model, in which states represent speaker specific distributions and transitions between states represent speaker turns. As in the ivector or JFA models, speaker distributions are modeled by GMMs with parameters constrained by eigenvoice priors. This allows to robustly estimate the speaker models from very short speech segments. The model, which was released as open source code and has already been used by several labs, is fully described for the first time in this paper. We present results and the system is compared and combined with other state-of-the-art approaches. The model provides the best results reported so far on the CALLHOME dataset.
@inproceedings{BUT155067,
author="Mireia {Diez Sánchez} and Lukáš {Burget} and Pavel {Matějka}",
title="Speaker Diarization based on Bayesian HMM with Eigenvoice Priors",
booktitle="Proceedings of Odyssey 2018",
year="2018",
journal="Proceedings of Odyssey: The Speaker and Language Recognition Workshop Odyssey 2014, Joensuu, Finland",
volume="2018",
number="6",
pages="147--154",
publisher="International Speech Communication Association",
address="Les Sables d´Olonne",
doi="10.21437/Odyssey.2018-21",
issn="2312-2846",
url="https://www.fit.vut.cz/research/publication/11786/"
}