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Automatic detection of laryngeal pathologies in running speech based on the HMM transformation of the nonlinear dynamics

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dc.contributor Universitat de Vic. Escola Politècnica Superior
dc.contributor Universitat de Vic. Grup de Recerca en Tecnologies Digitals
dc.contributor International Conference on Non-Linear Speech Processing NOLISP (2013 : Bèlgica)
dc.contributor.author Travieso, Carlos M.
dc.contributor.author Alonso, Jesús B.
dc.contributor.author Orozco-Arroyave, J.R.
dc.contributor.author Solé-Casals, Jordi
dc.contributor.author Gallego Jutglà, Esteve
dc.date.accessioned 2014-01-10T13:01:18Z
dc.date.available 2014-01-10T13:01:18Z
dc.date.created 2013
dc.date.issued 2013
dc.identifier.citation Travieso, C. M., Alonso, J. B., Orozco-Arroyave, J. R., Solé-Casals, J., & Gallego-Jutglà, E. (2013). Automatic detection of laryngeal pathologies in running speech based on the HMM transformation of the nonlinear dynamics A: Lecture Notes in Computer Science, 7911 LNAI pp. 136-143 ca_ES
dc.identifier.issn 0302-9743
dc.identifier.uri http://hdl.handle.net/10854/2625
dc.description.abstract This work describes a novel system for characterizing Laryngeal Pathologies using nonlinear dynamics, considering different complexity measures that are mainly based on the analysis of the time delay embedded space. The model is done by a kernel applied on Hidden Markov Model and decision of the Laryngeal pathology/control detection is performed by Support Vector Machine. Our system reaches accuracy up to 98.21%, improving the current reported results in the state of the art in the automatic classification of pathological speech signals (running speech) and showing the robustness of this proposal. ca_ES
dc.format application/pdf
dc.format.extent 8 p. ca_ES
dc.language.iso eng ca_ES
dc.publisher Springer ca_ES
dc.rights (c) Springer (The original publication is available at www.springerlink.com)
dc.rights Tots els drets reservats ca_ES
dc.subject.other Processos de Markov ca_ES
dc.subject.other Processament de la parla ca_ES
dc.title Automatic detection of laryngeal pathologies in running speech based on the HMM transformation of the nonlinear dynamics ca_ES
dc.type info:eu-repo/semantics/conferenceObject ca_ES
dc.identifier.doi https://doi.org/10.1007/978-3-642-38847-7-18
dc.relation.publisherversion http://link.springer.com/chapter/10.1007%2F978-3-642-38847-7_18
dc.rights.accesRights info:eu-repo/semantics/closedAccess ca_ES

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