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On the determination of epsilon during discriminative GMM training

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dc.contributor.authorGuido, Rodrigo Capobianco [UNESP]
dc.contributor.authorChen, Shi-Huang [UNESP]
dc.contributor.authorJunior, Sylvio Barbon [UNESP]
dc.contributor.authorSouza, Leonardo Mendes [UNESP]
dc.contributor.authorVieira, Lucimar Sasso [UNESP]
dc.contributor.authorRodrigues, Luciene Cavalcanti [UNESP]
dc.contributor.authorEscola, Joao Paulo Lemos [UNESP]
dc.contributor.authorZulato, Paulo Ricardo Franchi [UNESP]
dc.contributor.authorLacerda, Michel Alves [UNESP]
dc.contributor.authorRibeiro, Jussara [UNESP]
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionShu-Te University
dc.date.accessioned2014-05-27T11:25:20Z
dc.date.available2014-05-27T11:25:20Z
dc.date.issued2010-12-01
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.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.format.extent362-364
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.doi10.1109/ISM.2010.66
dc.identifier.lattes6542086226808067
dc.identifier.orcid0000-0002-0924-8024
dc.identifier.scopus2-s2.0-79951728004
dc.identifier.urihttp://hdl.handle.net/11449/72054
dc.language.isoeng
dc.relation.ispartofProceedings - 2010 IEEE International Symposium on Multimedia, ISM 2010
dc.rights.accessRightsAcesso aberto
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.typeTrabalho apresentado em evento
dcterms.licensehttp://www.ieee.org/publications_standards/publications/rights/rights_policies.html
dspace.entity.typePublication
unesp.author.lattes6542086226808067[1]
unesp.author.orcid0000-0002-0924-8024[1]
unesp.campusUniversidade Estadual Paulista (Unesp), Instituto de Biociências Letras e Ciências Exatas, São José do Rio Pretopt
unesp.departmentCiências da Computação e Estatística - IBILCEpt

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São José do Rio Preto - IBILCE - Instituto de Biociências, Letras e Ciências Exatas