Automatic language identification using deep neural networks

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dc.contributor.author López-Moreno, Ignacio
dc.contributor.author González-Domínguez, Javier
dc.contributor.author Oldrich, Plchot
dc.contributor.author Martínez, David R.
dc.contributor.author González-Rodríguez, Joaquín
dc.contributor.other UAM. Departamento de Tecnología Electrónica y de las Comunicaciones es_ES
dc.date.accessioned 2015-06-16T16:02:57Z
dc.date.available 2015-06-16T16:02:57Z
dc.date.issued 2014-01-01
dc.identifier.citation IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), IEEE, 2014. 5337-5341 en_US
dc.identifier.issn 1520-6149 (print) en_US
dc.identifier.uri http://hdl.handle.net/10486/666848
dc.description Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. I. López-Moreno, J. González-Domínguez, P. Oldrich, D. R. Martínez, J. González-Rodríguez, "Automatic language identification using deep neural networks", IEEE International Conference on Acoustics, Speech, and Signal Processing ICASSP, Florence (Italy), 2014 en_US
dc.description.abstract This work studies the use of deep neural networks (DNNs) to address automatic language identification (LID). Motivated by their recent success in acoustic modelling, we adapt DNNs to the problem of identifying the language of a given spoken utterance from short-term acoustic features. The proposed approach is compared to state-of-the-art i-vector based acoustic systems on two different datasets: Google 5M LID corpus and NIST LRE 2009. Results show how LID can largely benefit from using DNNs, especially when a large amount of training data is available. We found relative improvements up to 70%, in Cavg, over the baseline system. en_US
dc.format.extent 6 pág. es_ES
dc.format.mimetype application/pdf en_US
dc.language.iso eng e
dc.publisher IEEE en_US
dc.relation.ispartof ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings en_US
dc.rights © 2014 IEEE en_US
dc.subject.other Automatic Language Identification en_US
dc.subject.other DNNs en_US
dc.subject.other i-vectors en_US
dc.title Automatic language identification using deep neural networks en_US
dc.type conferenceObject en_US
dc.subject.eciencia Telecomunicaciones es_ES
dc.relation.publisherversion http://dx.doi.org/10.1109/ICASSP.2014.6854622
dc.identifier.doi 10.1109/ICASSP.2014.6854622
dc.identifier.publicationfirstpage 5337
dc.identifier.publicationlastpage 5341
dc.relation.eventdate May 4-9, 2014 en_US
dc.relation.eventplace Florence (Italy) en_US
dc.relation.eventtitle ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing en_US
dc.type.version info:eu-repo/semantics/acceptedVersion en_US
dc.contributor.group Análisis y Tratamiento de Voz y Señales Biométricas (ING EPS-002) es_ES
dc.rights.accessRights openAccess en_US


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