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dc.contributor.authorParalic, M.
dc.contributor.authorJarina, R.
dc.date.accessioned2016-03-24T06:43:45Z
dc.date.available2016-03-24T06:43:45Z
dc.date.issued2007-09cs
dc.identifier.citationRadioengineering. 2007, vol. 16, č. 3, s. 138-144. ISSN 1210-2512cs
dc.identifier.issn1210-2512
dc.identifier.urihttp://hdl.handle.net/11012/57315
dc.description.abstractThe paper addresses a novel algorithm for speaker searching and indexation based on unsupervised GMM training. The proposed method doesn\'t require a predefined set of generic background models, and the GMM speaker models are trained only from test samples. The constrain of the method is that the number of the speakers has to be known in advance. The results of initial experiments show that the proposed training method enables to create precise GMM speaker models from only a small amount of training data.en
dc.formattextcs
dc.format.extent138-144cs
dc.format.mimetypeapplication/pdfen
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttp://www.radioeng.cz/fulltexts/2007/07_03_138_144.pdfcs
dc.rightsCreative Commons Attribution 3.0 Unported Licenseen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/en
dc.subjectSpeaker indexingen
dc.subjecttrainingen
dc.subjectiterationen
dc.subjectlog. likelihooden
dc.subjectGaussian Mixture Model (GMM)en
dc.titleIterative Unsupervised GMM Training for Speaker Indexingen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
dc.coverage.issue3cs
dc.coverage.volume16cs
dc.rights.accessopenAccessen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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