Time Frequency Principal Components: Application to Speaker Identification

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dc.citation.bibtexNameinproceedingsen_US
dc.citation.conferenceNameProceedings of EUROSPEECHen_US
dc.contributor.authorMagrin-Chagnolleau, Ivanen_US
dc.contributor.authorDurou, Geoffreyen_US
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)en_US
dc.date.accessioned2007-10-31T00:52:23Z
dc.date.available2007-10-31T00:52:23Z
dc.date.issued1999-01-01en
dc.date.modified2004-11-05en_US
dc.date.note2004-01-14en_US
dc.date.submitted1999-01-01en_US
dc.descriptionConference Paperen_US
dc.description.abstractIn this paper, we propose a formalism, called vector filtering of spectral trajectories, which allows to integrate under a common formalism a lot of speech parameterization approaches. We then propose a new filtering in this framework, called time-frequency principal components (TFPC) of speech. We apply this new filtering in the framework of speaker identification, using a subset of the POLYCOST database. The results show an improvement of roughly 20% compared to the use of the classical cepstral coefficients augmented by their Delta-coefficients.en_US
dc.identifier.citationI. Magrin-Chagnolleau and G. Durou, "Time Frequency Principal Components: Application to Speaker Identification," 1999.
dc.identifier.urihttps://hdl.handle.net/1911/20076
dc.language.isoeng
dc.subjectTemporary*
dc.subject.keywordTemporaryen_US
dc.subject.otherMultifractalsen_US
dc.titleTime Frequency Principal Components: Application to Speaker Identificationen_US
dc.typeConference paper
dc.type.dcmiText
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