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dc.contributor.authorShannon, Benjamin J
dc.contributor.authorPaliwal, Kuldip K
dc.date.accessioned2017-05-03T13:01:06Z
dc.date.available2017-05-03T13:01:06Z
dc.date.issued2006
dc.date.modified2009-09-21T05:50:11Z
dc.identifier.issn0167-6393
dc.identifier.doi10.1016/j.specom.2006.08.003
dc.identifier.urihttp://hdl.handle.net/10072/14344
dc.description.abstractIn this paper, a feature extraction method that is robust to additive background noise is proposed for automatic speech recognition. Since the background noise corrupts the autocorrelation coefficients of the speech signal mostly at the lower-time lags, while the higher-lag autocorrelation coefficients are least affected, this method discards the lower-lag autocorrelation coefficients and uses only the higher-lag autocorrelation coefficients for spectral estimation. The magnitude spectrum of the windowed higher-lag autocorrelation sequence is used here as an estimate of the power spectrum of the speech signal. This power spectral estimate is processed further (like the well-known Mel frequency cepstral coefficient (MFCC) procedure) by the Mel filter bank, log operation and the discrete cosine transform to get the cepstral coefficients. These cepstral coefficients are referred to as the autocorrelation Mel frequency cepstral coefficients (AMFCCs). We evaluate the speech recognition performance of the AMFCC features on the Aurora and the resource management databases and show that they perform as well as the MFCC features for clean speech and their recognition performance is better than the MFCC features for noisy speech. Finally, we show that the AMFCC features perform better than the features derived from the robust linear prediction-based methods for noisy speech.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.publisher.placeNetherlands
dc.publisher.urihttp://www.elsevier.com/wps/find/journaldescription.cws_home/505597/description#description
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom1458
dc.relation.ispartofpageto1485
dc.relation.ispartofjournalSpeech Communication
dc.relation.ispartofvolume48
dc.rights.retentionY
dc.subject.fieldofresearchCognitive and computational psychology
dc.subject.fieldofresearchLinguistics
dc.subject.fieldofresearchcode5204
dc.subject.fieldofresearchcode4704
dc.titleFeature extraction from higher-lag autocorrelation coefficients for robust speech recognition
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.facultyGriffith Sciences, Griffith School of Engineering
gro.date.issued2006
gro.hasfulltextNo Full Text
gro.griffith.authorPaliwal, Kuldip K.


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