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dc.contributor.advisorVarona Fernández, María Amparo
dc.contributor.advisorBordel García, Germán ORCID
dc.contributor.authorDíez Sánchez, Mireia
dc.contributor.otherElectricidad y Electrónica;;Elektrizitatea eta Elektronikaes
dc.date.accessioned2015-11-11T08:33:43Z
dc.date.available2015-11-11T08:33:43Z
dc.date.issued2015-09-04
dc.date.submitted2015-09-04
dc.identifier.urihttp://hdl.handle.net/10810/16088
dc.description150 p.es
dc.description.abstractThis Thesis, developed in the Software Technologies Working Group of the Departmentof Electricity and Electronics of the University of the Basque Country, focuseson the research eld of spoken language and speaker recognition technologies.More specically, the research carried out studies the design of a set of featuresconveying spectral acoustic and phonotactic information, searches for the optimalfeature extraction parameters, and analyses the integration and usage of the featuresin language recognition systems, and the complementarity of these approacheswith regard to state-of-the-art systems. The study reveals that systems trained onthe proposed set of features, denoted as Phone Log-Likelihood Ratios (PLLRs), arehighly competitive, outperforming in several benchmarks other state-of-the-art systems.Moreover, PLLR-based systems also provide complementary information withregard to other phonotactic and acoustic approaches, which makes them suitable infusions to improve the overall performance of spoken language recognition systems.The usage of this features is also studied in speaker recognition tasks. In this context,the results attained by the approaches based on PLLR features are not as remarkableas the ones of systems based on standard acoustic features, but they still providecomplementary information that can be used to enhance the overall performance ofthe speaker recognition systems.es
dc.language.isoenges
dc.rightsinfo:eu-repo/semantics/openAccesses
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectartificial intelligencees
dc.subjectcomputer softwarees
dc.subjectinformaticses
dc.subjectinteligencia artificiales
dc.subjectsoftwarees
dc.subjectinformáticaes
dc.titleFrame-level features conveying phonetic information for language and speaker recognitiones
dc.typeinfo:eu-repo/semantics/doctoralThesises
dc.rights.holder(cc)2015 MIREIA DIEZ SANCHEZ (cc by 4.0)
dc.identifier.studentID283812es
dc.identifier.projectID431es
dc.departamentoesElectricidad y electrónicaes_ES
dc.departamentoeuElektrizitatea eta elektronikaes_ES


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(cc)2015 MIREIA DIEZ SANCHEZ (cc by 4.0)
Except where otherwise noted, this item's license is described as (cc)2015 MIREIA DIEZ SANCHEZ (cc by 4.0)