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dc.contributor.authorGhahabi Esfahani, Omid
dc.contributor.authorHernando Pericás, Francisco Javier
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.date.accessioned2015-04-16T16:30:47Z
dc.date.created2014-11-19
dc.date.issued2014-11-19
dc.identifier.citationGhahabi, O.; Hernando, J. Global impostor selection for DBNs in multi-session i-vector speaker recognition. "Lecture notes in computer science", 19 Novembre 2014, vol. LNAI 8854, p. 89-98.
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/2117/27397
dc.description.abstractAn effective global impostor selection method is proposed in this paper for discriminative Deep Belief Networks (DBN) in the context of a multi-session i-vector based speaker recognition. The proposed method is an iterative process in which in each iteration the whole impostor i-vector dataset is divided randomly into two subsets. The impostors in one subset which are closer to each impostor in another subset are selected and impostor frequencies are computed. At the end, those impostors with higher frequencies will be the global selected ones. They are then clustered and the centroids are considered as the final impostors for the DBN speaker models. The advantage of the proposed method is that in contrary to other similar approaches, only the background i-vector dataset is employed. The experimental results are performed on the NIST 2014 i-vector challenge dataset and it is shown that the proposed selection method improves the performance of the DBN-based system in terms of minDCF by 7% and the whole system outperforms the baseline in the challenge by more than 22% relative improvement.
dc.format.extent10 p.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la parla i del senyal acústic
dc.subjectÀrees temàtiques de la UPC::Informàtica
dc.subject.lcshAutomatic speech recognition
dc.subject.otherSpeaker recognition
dc.subject.otherDeep belief network
dc.subject.otherImpostor selection
dc.subject.otherNIST i-vector challenge
dc.titleGlobal impostor selection for DBNs in multi-session i-vector speaker recognition
dc.typeArticle
dc.subject.lemacReconeixement automàtic de la parla
dc.contributor.groupUniversitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla
dc.identifier.doi10.1007/978-3-319-13623-3_10
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://link.springer.com/chapter/10.1007%2F978-3-319-13623-3_10
dc.rights.accessRestricted access - publisher's policy
drac.iddocument15429563
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
upcommons.citation.authorGhahabi, O.; Hernando, J.
upcommons.citation.publishedtrue
upcommons.citation.publicationNameLecture notes in computer science
upcommons.citation.volumeLNAI 8854
upcommons.citation.startingPage89
upcommons.citation.endingPage98


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