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dc.contributor.authorGuido, Rodrigo Capobianco-
dc.contributor.authorChen, Shi-Huang-
dc.contributor.authorJunior, Sylvio Barbon-
dc.contributor.authorSouza, Leonardo Mendes-
dc.contributor.authorVieira, Lucimar Sasso-
dc.contributor.authorRodrigues, Luciene Cavalcanti-
dc.contributor.authorEscola, Joao Paulo Lemos-
dc.contributor.authorZulato, Paulo Ricardo Franchi-
dc.contributor.authorLacerda, Michel Alves-
dc.contributor.authorRibeiro, Jussara-
dc.date.accessioned2014-05-27T11:25:20Z-
dc.date.accessioned2016-10-25T18:32:55Z-
dc.date.available2014-05-27T11:25:20Z-
dc.date.available2016-10-25T18:32:55Z-
dc.date.issued2010-12-01-
dc.identifierhttp://dx.doi.org/10.1109/ISM.2010.66-
dc.identifier.citationProceedings - 2010 IEEE International Symposium on Multimedia, ISM 2010, p. 362-364.-
dc.identifier.urihttp://hdl.handle.net/11449/72054-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/72054-
dc.description.abstractDiscriminative training of Gaussian Mixture Models (GMMs) for speech or speaker recognition purposes is usually based on the gradient descent method, in which the iteration step-size, ε, uses to be defined experimentally. In this letter, we derive an equation to adaptively determine ε, by showing that the second-order Newton-Raphson iterative method to find roots of equations is equivalent to the gradient descent algorithm. © 2010 IEEE.en
dc.format.extent362-364-
dc.language.isoeng-
dc.sourceScopus-
dc.subjectDiscriminative training of Gaussian Mixture Models (GMMs)-
dc.subjectMarkov Models-
dc.subjectSpeaker identification-
dc.subjectSpeech recognition-
dc.subjectDiscriminative training-
dc.subjectGaussian mixture models-
dc.subjectGradient descent algorithms-
dc.subjectGradient Descent method-
dc.subjectIteration step-
dc.subjectNewton-Raphson iterative method-
dc.subjectSecond orders-
dc.subjectSpeaker recognition-
dc.subjectGaussian distribution-
dc.subjectIterative methods-
dc.subjectLoudspeakers-
dc.subjectMarkov processes-
dc.titleOn the determination of epsilon during discriminative GMM trainingen
dc.typeoutro-
dc.contributor.institutionUniversidade de São Paulo (USP)-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.contributor.institutionShu-Te University-
dc.description.affiliationSpeechLab. FFI, Institute of Physics at São Carlos University of São Paulo, Av. Trabalhador São Carlense 400, 13566-590, São Carlos, SP-
dc.description.affiliationDCCE/IBILCE/UNESP São Paulo State University, Rua Cristovão Colombo 2265, São José do Rio Preto, SP-
dc.description.affiliationDepartment of Computer Science and Information Engineering Shu-Te University, N.59, Hengshan Rd., Yanchao, Kaohsiung County 82445-
dc.description.affiliationUnespDCCE/IBILCE/UNESP São Paulo State University, Rua Cristovão Colombo 2265, São José do Rio Preto, SP-
dc.identifier.doi10.1109/ISM.2010.66-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartofProceedings - 2010 IEEE International Symposium on Multimedia, ISM 2010-
dc.identifier.scopus2-s2.0-79951728004-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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