Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/44083
Campo DC Valoridioma
dc.contributor.authorHenriquez Rodriguez, Patriciaen_US
dc.contributor.authorAlonso Hernández, Jesús Bernardinoen_US
dc.contributor.authorFerrer Ballester, Miguel Ángelen_US
dc.contributor.authorTravieso González, Carlos Manuelen_US
dc.contributor.authorGodino Llorente, Juan Ignacioen_US
dc.contributor.authorDíaz-de-María, Fernandoen_US
dc.contributor.otherGodino Llorente, Juan Ignacio-
dc.contributor.otherFerrer, Miguel A-
dc.contributor.otherDiaz de Maria, Fernando-
dc.contributor.otherTravieso-Gonzalez, Carlos M.-
dc.contributor.otherHenriquez Rodriguez, Patricia-
dc.date.accessioned2018-11-21T20:06:01Z-
dc.date.available2018-11-21T20:06:01Z-
dc.date.issued2009en_US
dc.identifier.issn1558-7916en_US
dc.identifier.urihttp://hdl.handle.net/10553/44083-
dc.description.abstractIn this paper, we propose to quantify the quality of the recorded voice through objective nonlinear measures. Quantification of speech signal quality has been traditionally carried out with linear techniques since the classical model of voice production is a linear approximation. Nevertheless, nonlinear behaviors in the voice production process have been shown. This paper studies the usefulness of six nonlinear chaotic measures based on nonlinear dynamics theory in the discrimination between two levels of voice quality: healthy and pathological. The studied measures are first- and second-order Renyi entropies, the correlation entropy and the correlation dimension. These measures were obtained from the speech signal in the phase-space domain. The values of the first minimum of mutual information function and Shannon entropy were also studied. Two databases were used to assess the usefulness of the measures: a multiquality database composed of four levels of voice quality (healthy voice and three levels of pathological voice); and a commercial database (MEEI Voice Disorders) composed of two levels of voice quality (healthy and pathological voices). A classifier based on standard neural networks was implemented in order to evaluate the measures proposed. Global success rates of 82.47% (multiquality database) and 99.69% (commercial database) were obtained.en_US
dc.languagespaen_US
dc.publisher1558-7916-
dc.relation.ispartofIEEE Transactions on Audio, Speech and Language Processingen_US
dc.sourceIEEE Transactions on Audio, Speech and Language Processing[ISSN 1558-7916],v. 17, p. 1186-1195en_US
dc.subject3307 Tecnología electrónicaen_US
dc.subject.otherPathology , Databases , Entropy , Speech , Linear approximation , Chaos , Phase measurement , Mutual information , Measurement standards , Neural networks , Chaos , disordered speech , entropy , nonlinearityen_US
dc.titleCharacterization of Healthy and Pathological Voice Through Measures Based on Nonlinear Dynamicsen_US
dc.typeinfo:eu-repo/semantics/Articleen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/TASL.2009.2016734
dc.identifier.scopus85008035391-
dc.identifier.isi000268039700011-
dcterms.isPartOfIeee Transactions On Audio Speech And Language Processing-
dcterms.sourceIeee Transactions On Audio Speech And Language Processing[ISSN 1558-7916],v. 17 (6), p. 1186-1195-
dc.contributor.authorscopusid57027894900-
dc.contributor.authorscopusid24774957200-
dc.contributor.authorscopusid55636321172-
dc.contributor.authorscopusid6602376272-
dc.contributor.authorscopusid6506282623-
dc.contributor.authorscopusid6602882055-
dc.description.lastpage1195-
dc.description.firstpage1186-
dc.relation.volume17-
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.identifier.wosWOS:000268039700011-
dc.contributor.daisngid1432987-
dc.contributor.daisngid982072-
dc.contributor.daisngid29084685
dc.contributor.daisngid233119-
dc.contributor.daisngid265761-
dc.contributor.daisngid574796-
dc.contributor.daisngid624552-
dc.identifier.investigatorRIDF-5855-2016-
dc.identifier.investigatorRIDL-3863-2013-
dc.identifier.investigatorRIDE-8048-2011-
dc.identifier.investigatorRIDN-5967-2014-
dc.identifier.investigatorRIDNo ID-
dc.identifier.externalWOS:000268039700011-
dc.contributor.wosstandardWOS:Henriquez, P
dc.contributor.wosstandardWOS:Alonso, JB
dc.contributor.wosstandardWOS:Ferrer, MA
dc.contributor.wosstandardWOS:Travieso, CM
dc.contributor.wosstandardWOS:Godino-Llorente, JI
dc.contributor.wosstandardWOS:Diaz-de-Maria, F
dc.date.coverdateEnero 2009
dc.identifier.ulpgces
dc.description.jcr1,782
dc.description.jcrqQ1
item.grantfulltextnone-
item.fulltextSin texto completo-
crisitem.author.deptDepartamento de Ciencias Clínicas-
crisitem.author.deptGIR IDeTIC: División de Procesado Digital de Señales-
crisitem.author.deptIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.deptDepartamento de Señales y Comunicaciones-
crisitem.author.deptGIR IDeTIC: División de Procesado Digital de Señales-
crisitem.author.deptIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.deptDepartamento de Señales y Comunicaciones-
crisitem.author.deptGIR IDeTIC: División de Procesado Digital de Señales-
crisitem.author.deptIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.deptDepartamento de Señales y Comunicaciones-
crisitem.author.orcid0000-0002-1170-2820-
crisitem.author.orcid0000-0002-7866-585X-
crisitem.author.orcid0000-0002-2924-1225-
crisitem.author.orcid0000-0002-4621-2768-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.fullNameHenríquez Sánchez, Patricia-
crisitem.author.fullNameAlonso Hernández, Jesús Bernardino-
crisitem.author.fullNameFerrer Ballester, Miguel Ángel-
crisitem.author.fullNameTravieso González, Carlos Manuel-
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