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

Travieso, Carlos M.; Alonso, Jesús B.; Orozco-Arroyave, J.R.; Solé-Casals, Jordi; Gallego Jutglà, Esteve

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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.accessRights	info:eu-repo/semantics/closedAccess	ca_ES