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DC poleHodnotaJazyk
dc.contributor.authorKolář, Jáchym
dc.contributor.authorMüller, Luděk
dc.date.accessioned2016-01-06T09:26:28Z
dc.date.available2016-01-06T09:26:28Z
dc.date.issued2003
dc.identifier.citationKOLÁŘ, Jáchym; MÜLLER, Luděk. The application of bayesian information criterion in acoustic model refinement. In: Call for papers ECMS 2003: 6 th international workshop on electronics, control, measurement, and signals 2003, 2nd-4th June 2003, Technical University of Liberec Liberec, Czech Republic. Liberec: Technical University of Liberec, 20003, p. 44-48. ISBN 80-7083-708-Xen
dc.identifier.isbn80-7083-708-X
dc.identifier.urihttp://www.kky.zcu.cz/cs/publications/KolarJ_2003_TheApplicationof
dc.identifier.urihttp://hdl.handle.net/11025/17118
dc.format5 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherTechnical University of Liberecen
dc.rights© Jáchym Kolář - Luděk Müllercs
dc.subjectbayesinova informační kritériacs
dc.subjectakustické modelovánícs
dc.subjectskryté Markovovy modelycs
dc.subjectautomatické rozpoznávání řečics
dc.titleThe application of Bayesian information criterion in acoustic model refinementen
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedAutomatic speech recognition (ASR) systems usually consist of an acoustic model and a language model. This paper describes a technique of an efficient deployment of the acoustic model parameters. The acoustic model typically utilizes Continuous Density Hidden Markov Models (CDHMM). The output probability of a particular CDHMM state is represented by a Gaussian mixture density with a diagonal covariance structure. Usually, the output probability density function of each CDHMM state contains the same number of mixture components although a different number of components in individual states may yield more accurate recognition results, especially for low-resource ASR systems. The central idea is to assign more components to states where it is effective and less components to states where the increasing number of components is not warranting a significantly better description of the training data. The number of mixture components for a particular CDHMM state is chosen by optimizing the Bayesian Information Criterion (BIC).en
dc.subject.translatedbayesian information criterionen
dc.subject.translatedacoustic modelingen
dc.subject.translatedhidden Markov modelsen
dc.subject.translatedautomatic speech recognitionen
dc.type.statusPeer-revieweden
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