Conference paper

DIEZ Sánchez Mireia, BURGET Lukáš and MATĚJKA Pavel. Speaker Diarization based on Bayesian HMM with Eigenvoice Priors. In: Proceedings of Odyssey 2018. Les Sables d´Olonne: International Speech Communication Association, 2018, pp. 147-154. ISSN 2312-2846.
Publication language:english
Original title:Speaker Diarization based on Bayesian HMM with Eigenvoice Priors
Title (cs):Diarizace založená na bayessovském HMM s Eigenvoice apriorní informací
Pages:147-154
Proceedings:Proceedings of Odyssey 2018
Conference:Odyssey 2018
Place:Les Sables d´Olonne, FR
Year:2018
Journal:Proceedings of Odyssey: The Speaker and Language Recognition Workshop, Vol. 2018, No. 6, 4 Rue des Fauvettes - Lous Tourils, F-66390 BAIXAS, FR
ISSN:2312-2846
DOI:10.21437/Odyssey.2018-21
Publisher:International Speech Communication Association
URL:http://www.fit.vutbr.cz/research/groups/speech/publi/2018/diez_odyssey2018_63.pdf [PDF]
Keywords
Speaker diarization, speaker recognition
Annotation
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.
BibTeX:
@INPROCEEDINGS{
   author = {Mireia S{\'{a}}nchez Diez and Luk{\'{a}}{\v{s}}
	Burget and Pavel Mat{\v{e}}jka},
   title = {Speaker Diarization based on Bayesian HMM with
	Eigenvoice Priors},
   pages = {147--154},
   booktitle = {Proceedings of Odyssey 2018},
   journal = {Proceedings of Odyssey: The Speaker and Language Recognition
	Workshop},
   volume = {2018},
   number = {6},
   year = {2018},
   location = {Les Sables dOlonne, FR},
   publisher = {International Speech Communication Association},
   ISSN = {2312-2846},
   doi = {10.21437/Odyssey.2018-21},
   language = {english},
   url = {http://www.fit.vutbr.cz/research/view_pub.php.en.iso-8859-2?id=11786}
}

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