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dc.contributor.authorSafari, Pooyan
dc.contributor.authorGhahabi, Omid
dc.contributor.authorHernando Pericás, Francisco Javier
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.date.accessioned2016-03-16T15:01:42Z
dc.date.issued2015
dc.identifier.citationSafari, P., Ghahabi, O., Hernando, J. Feature classification by means of Deep Belief Networks for speaker recognition. A: European Signal Processing Conference. "23rd European Signal Processing Conference (EUSIPCO) took place 31 August - 4 September 2015 in Nice, France". Niza: Institute of Electrical and Electronics Engineers (IEEE), 2015, p. 2162-2166.
dc.identifier.isbn978-0-9928626-4-0
dc.identifier.urihttp://hdl.handle.net/2117/84516
dc.description.abstractIn this paper, we propose to discriminatively model target and impostor spectral features using Deep Belief Networks (DBNs) for speaker recognition. In the feature level, the number of impostor samples is considerably large compared to previous works based on i-vectors. Therefore, those i-vector based impostor selection algorithms are not computationally practical. On the other hand, the number of samples for each target speaker is different from one speaker to another which makes the training process more difficult. In this work, we take advantage of DBN unsupervised learning to train a global model, which will be referred to as Universal DBN (UDBN). Then we adapt this UDBN to the data of each target speaker. The evaluation is performed on the core test condition of the NIST SRE 2006 database and it is shown that the proposed architecture achieves more than 8% relative improvement in comparison to the conventional Multilayer Perceptron (MLP).
dc.format.extent5 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la parla i del senyal acústic
dc.subject.lcshAutomatic speech recognition
dc.subject.otherSpeaker recognition
dc.subject.otherDeep Belief Network
dc.subject.otherRestricted Boltzmann Machine
dc.subject.otherFeature classification
dc.titleFeature classification by means of Deep Belief Networks for speaker recognition
dc.typeConference report
dc.subject.lemacReconeixement automàtic de la parla
dc.contributor.groupUniversitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://www.google.es/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0ahUKEwixwsfg1J3LAhUD6xQKHb8xD-EQFggjMAA&url=http%3A%2F%2Fwww.eurasip.org%2FProceedings%2FEusipco%2FEusipco2015%2Fpapers%2F1570104993.pdf&usg=AFQjCNHYI_epishkVfFETkmAtxHK7l44Qg&cad=rja
dc.rights.accessRestricted access - publisher's policy
drac.iddocument17531947
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
upcommons.citation.authorSafari, P., Ghahabi, O., Hernando, J.
upcommons.citation.contributorEuropean Signal Processing Conference
upcommons.citation.pubplaceNiza
upcommons.citation.publishedtrue
upcommons.citation.publicationName23rd European Signal Processing Conference (EUSIPCO) took place 31 August - 4 September 2015 in Nice, France
upcommons.citation.startingPage2162
upcommons.citation.endingPage2166


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