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dc.contributor.author | Sanchis Navarro, José Alberto | es_ES |
dc.contributor.author | Juan Císcar, Alfonso | es_ES |
dc.contributor.author | Vidal, Enrique | es_ES |
dc.date.accessioned | 2014-03-03T08:46:06Z | |
dc.date.issued | 2012-02 | |
dc.identifier.issn | 1558-7916 | |
dc.identifier.uri | http://hdl.handle.net/10251/36083 | |
dc.description.abstract | Confidence estimation has been largely used in speech recognition to detect words in the recognized sentence that have been likely misrecognized. Confidence estimation can be seen as a conventional pattern classification problem in which a set of features is obtained for each hypothesized word in order to classify it as either correct or incorrect. We propose a smoothed naïve Bayes classification model to profitably combine these features. The model itself is a combination of word-dependent (specific) and word-independent (generalized) naïve Bayes models. As in statistical language modeling, the purpose of the generalized model is to smooth the (class posterior) estimates given by the specific models. Our classification model is empirically compared with confidence estimation based on posterior probabilities computed on word graphs. Empirical results clearly show that the good performance of word graph-based posterior probabilities can be improved by using the naïve Bayes combination of features. | es_ES |
dc.description.sponsorship | This work was supported by the EC (FEDER/FSE), the Spanish Government (MICINN, MITyC, "Plan E," under grants MIPRCV "Consolider Ingenio 2010" CSD2007-00018, iTrans2 TIN2009-14511, and erudito.com TSI-020110-2009-439), the Generalitat Valenciana (grants Prometeo/2009/014 and GV/2010/067), and the Universitat Politecnica de Valencia (grant 20091027). The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Mark J. F. Gales. | en_EN |
dc.format.extent | 10 | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | es_ES |
dc.relation.ispartof | IEEE Transactions on Audio, Speech and Language Processing | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Automatic speech recognition (ASR) | es_ES |
dc.subject | Confidence measures | es_ES |
dc.subject | Naïve Bayes | es_ES |
dc.subject | Smoothing | es_ES |
dc.subject | Posterior probabilities | es_ES |
dc.subject | Word graphs | es_ES |
dc.subject.classification | LENGUAJES Y SISTEMAS INFORMATICOS | es_ES |
dc.title | A word-based naïve bayes classifier for confidence estimation in speech recognition | es_ES |
dc.type | Artículo | es_ES |
dc.embargo.lift | 10000-01-01 | |
dc.embargo.terms | forever | es_ES |
dc.identifier.doi | 10.1109/TASL.2011.2162403 | |
dc.relation.projectID | info:eu-repo/grantAgreement/MEC//CSD2007-00018/ES/Multimodal Intraction in Pattern Recognition and Computer Visionm/ / | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/GVA//GV%2F2010%2F067/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/GVA//PROMETEO09%2F2009%2F014/ES/Adaptive learning and multimodality in pattern recognition (Almapater)/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/UPV//20091027/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MICINN//TIN2009-14511/ES/Traduccion De Textos Y Transcripcion De Voz Interactivas/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MITURCO//TSI-020110-2009-0439/ES/ERUDITO.COM/ | es_ES |
dc.rights.accessRights | Cerrado | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Instituto Universitario Mixto Tecnológico de Informática - Institut Universitari Mixt Tecnològic d'Informàtica | es_ES |
dc.description.bibliographicCitation | Sanchis Navarro, JA.; Juan Císcar, A.; Vidal, E. (2012). A word-based naïve bayes classifier for confidence estimation in speech recognition. IEEE Transactions on Audio, Speech and Language Processing. 20(2):565-574. https://doi.org/10.1109/TASL.2011.2162403 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | http://dx.doi.org/10.1109/TASL.2011.2162403 | es_ES |
dc.description.upvformatpinicio | 565 | es_ES |
dc.description.upvformatpfin | 574 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 20 | es_ES |
dc.description.issue | 2 | es_ES |
dc.relation.senia | 208626 | |
dc.contributor.funder | Generalitat Valenciana | es_ES |
dc.contributor.funder | Universitat Politècnica de València | es_ES |
dc.contributor.funder | European Commission | es_ES |