A Robust to Outliers Hidden Markov Model with Application in Text-Dependent Speaker Identification

Chatzis, S; Varvarigou, T

dc.contributor.author	Chatzis, S	en
dc.contributor.author	Varvarigou, T	en
dc.date.accessioned	2014-03-01T02:50:56Z
dc.date.available	2014-03-01T02:50:56Z
dc.date.issued	2007	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/35224
dc.subject	Gaussian Mixture Model	en
dc.subject	Heavy Tail	en
dc.subject	Mixture Model	en
dc.subject	Pattern Recognition	en
dc.subject	Speaker Identification	en
dc.subject	Speaker Recognition	en
dc.subject	Hidden Markov Chain	en
dc.subject	Hidden Markov Model	en
dc.title	A Robust to Outliers Hidden Markov Model with Application in Text-Dependent Speaker Identification	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ICSPC.2007.4728441	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ICSPC.2007.4728441	en
heal.publicationDate	2007	en
heal.abstract	Hidden Markov models using Gaussian mixture models as their hidden state distributions have been successfully applied in text-dependent speaker identification applications. Nevertheless, it is well-known that Gaussian mixture models are very vulnerable to the presence of outliers in the fitting set used for their estimation. Student's-t mixture models have been proposed recently as a heavy-tailed, tolerant to outliers alternative to	en
heal.journalName	IEEE International Conference on Signal Processing and Communications	en
dc.identifier.doi	10.1109/ICSPC.2007.4728441	en