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dc.contributor.authorPaliwal, Kuldipen_US
dc.date.accessioned2017-04-24T10:06:23Z
dc.date.available2017-04-24T10:06:23Z
dc.date.issued2006en_US
dc.date.modified2009-09-21T05:50:12Z
dc.identifier.issn15587916en_US
dc.identifier.doi10.1109/TSA.2005.855834en_AU
dc.identifier.urihttp://hdl.handle.net/10072/14345
dc.description.abstractWe investigate how dominant-frequency information can be used in speech feature extraction to increase the robustness of automatic speech recognition against additive background noise. First, we review several earlier proposed auditory-based feature extraction methods and argue that the use of dominant-frequency information might be one of the major reasons for their improved noise robustness. Furthermore, we propose a new feature extraction method, which combines subband power information with dominant subband frequency information in a simple and computationally efficient way. The proposed features are shown to be considerably more robust against additive background noise than standard mel-frequency cepstrum coefficients on two different recognition tasks. The performance improvement increased as we moved from a small-vocabulary isolated-word task to a medium-vocabulary continuous-speech task, where the proposed features also outperformed a computationally expensive auditory-based method. The greatest improvement was obtained for noise types characterized by a relatively flat spectral density.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent46512 bytes
dc.format.extent619699 bytes
dc.format.mimetypetext/plain
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherIEEEen_US
dc.publisher.placeUSAen_US
dc.publisher.urihttp://ieeexplore.ieee.org/servlet/opac?punumber=10376en_AU
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofpagefrom600en_US
dc.relation.ispartofpageto608en_US
dc.relation.ispartofissue2en_US
dc.relation.ispartofjournalIEEE Transactions on Audio, Speech and Language Processingen_US
dc.relation.ispartofvolume14en_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchcode280206en_US
dc.titleRobust Speech Recognition in Noisy Environments Based on Subband Spectral Centroid Histogramsen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.facultyGriffith Sciences, Griffith School of Engineeringen_US
gro.rights.copyrightCopyright 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_AU
gro.date.issued2006
gro.hasfulltextFull Text


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