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dc.contributor.authorNogueiras Rodríguez, Albino
dc.contributor.authorMariño Acebal, José Bernardo
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.identifier.citationNogueiras, A.; Mariño, J. Task independent minimum confusability training for continuous speech recognition. A: IEEE International Conference on Acoustics, Speech and Signal Processing. "Proceedings of ICASSP'98". Seattle: 1998, p. 477-480.
dc.description.abstractIn this paper, a task independent discriminative training framework for subword units based continuous speech recognition is presented. Instead of aiming at the optimisation of any task independent figure, say the phone classification or recognition rates, we focus our attention to the reduction of the number of errors committed by the system when a task is defined. This consideration leads to the use of a segmental approach based on the minimisation of the confusability over short chains of subword units. Using this framework, a reduction of 32% in the string error rate may be achieved in the recognition of unknown length digit strings using task independent phone like units.
dc.format.extent4 p.
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament del senyal en les telecomunicacions
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Llenguatge natural
dc.subject.lcshAutomatic speech recognition
dc.subject.lcshArtificial intelligence
dc.titleTask independent minimum confusability training for continuous speech recognition
dc.typeConference lecture
dc.subject.lemacReconeixement automàtic de la parla
dc.subject.lemacIntel·ligència artificial
dc.contributor.groupUniversitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla
dc.description.peerreviewedPeer Reviewed
dc.rights.accessRestricted access - publisher's policy
dc.description.versionPostprint (published version)
upcommons.citation.authorNogueiras, A.; Mariño, J.
upcommons.citation.contributorIEEE International Conference on Acoustics, Speech and Signal Processing
upcommons.citation.publicationNameProceedings of ICASSP'98

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