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dc.contributor.authorErdoğan, A.
dc.contributor.authorDemiroğlu, Cenk
dc.date.accessioned2016-02-15T13:38:34Z
dc.date.available2016-02-15T13:38:34Z
dc.date.issued2010
dc.identifier.issn2165-0608
dc.identifier.urihttp://hdl.handle.net/10679/2377
dc.identifier.urihttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=5651366
dc.descriptionDue to copyright restrictions, the access to the full text of this article is only available via subscription.
dc.description.abstractGaussian mixture models (GMM) is one of the most commonly used methods in text-independent speaker identification systems. In this paper, performance of the GMM approach has been measured with different parameters and settings. Voice activity detection (VAD) component has been found to have a significant impact on the performance. Therefore, VAD algorithms that are robust to background noise have been proposed. Significant differences in performance have been observed between male and female speakers and GSM/PSTN channels. Moreover, single-stream GMM approach has been found to perform significantly better than the multi-stream GMM approach. It has been observed under all conditions that data duration is critical for good performance.
dc.language.isoturen_US
dc.publisherIEEE
dc.relation.ispartofSignal Processing and Communications Applications Conference (SIU), 2010 IEEE 18th
dc.rightsrestrictedAccess
dc.titleGauss karışım modeli tabanlı konuşmacı belirleme sistemlerinde klasik MAP uyarlanması yönteminin performans analizien_US
dc.title.alternativePerformance analysis of classical MAP adaptation in GMM-based speaker identification systems
dc.typeConference paperen_US
dc.peerreviewedyes
dc.publicationstatuspublisheden_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID144947
dc.contributor.ozuauthorDemiroğlu, Cenk
dc.identifier.startpage867
dc.identifier.endpage870
dc.identifier.doi10.1109/SIU.2010.5651366
dc.subject.keywordsGaussian processes
dc.subject.keywordsNoise
dc.subject.keywordsSpeaker recognition
dc.identifier.scopusSCOPUS:2-s2.0-78651453564


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