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dc.contributor.authorNakamura, Satoshien_US
dc.contributor.authorPaliwal, Kuldipen_US
dc.contributor.authorWang, Renhuaen_US
dc.contributor.authorZhu, Donglaien_US
dc.date.accessioned2017-04-04T16:59:42Z
dc.date.available2017-04-04T16:59:42Z
dc.date.issued2005en_US
dc.date.modified2009-09-21T05:50:02Z
dc.identifier.issn01676393en_US
dc.identifier.doi10.1016/j.specom.2005.02.006en_AU
dc.identifier.urihttp://hdl.handle.net/10072/4272
dc.description.abstractNoise-robust speech recognition has become an important area of research in recent years. In current speech recognition systems, the Mel-frequency cepstrum coefficients (MFCCs) are used as recognition features. When the speech signal is corrupted by narrow-band noise, the entire MFCC feature vector gets corrupted and it is not possible to exploit the frequency-selective property of the noise signal to make the recognition system robust. Recently, a number of sub-band speech recognition approaches have been proposed in the literature, where the full-band power spectrum is divided into several sub-bands and then the sub-bands are combined depending on their reliability. In conventional sub-band approaches the reliability can only be set experimentally or estimated during training procedures, which may not match the observed data and often causes degradation of performance. We propose a novel sub-band approach, where frequency sub-bands are multiplied with weighting factors and then combined and converted to cepstra, which have proven to be more robust than both full-band and conventional sub-band cepstra in our experiments. Furthermore, the weighting factors can be estimated by using maximum likelihood adaptation approaches in order to minimize the mismatch between trained models and observed features. We evaluated our methods on AURORA2 and Resource Management tasks and obtained consistent performance improvement on both tasks.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherElsevieren_US
dc.publisher.placeAmsterdam, The Netherlandsen_US
dc.publisher.urihttp://www.elsevier.com/wps/find/journaldescription.cws_home/505597/description#descriptionen_AU
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofpagefrom243en_US
dc.relation.ispartofpageto264en_US
dc.relation.ispartofjournalSpeech Communicationen_US
dc.relation.ispartofvolume47en_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchcode280206en_US
dc.titleMaximum likelihood sub-band adaptation for robust speech recognitionen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.rights.copyrightCopyright 2005 Elsevier : Reproduced in accordance with the copyright policy of the publisher : This journal is available online - use hypertext linksen_AU
gro.date.issued2005
gro.hasfulltextNo Full Text


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