Show simple item record

dc.contributor.convenorF. Castanie
dc.contributor.authorSo, Stephen
dc.contributor.authorPaliwal, Kuldip K
dc.contributor.editorF. Castanie
dc.date.accessioned2017-05-03T13:01:24Z
dc.date.available2017-05-03T13:01:24Z
dc.date.issued2006
dc.date.modified2009-09-21T05:50:08Z
dc.identifier.isbn978-1-4244-0468-1
dc.identifier.issn1520-6149
dc.identifier.refurihttp://www.icassp2006.org
dc.identifier.doi10.1109/ICASSP.2006.1659989
dc.identifier.urihttp://hdl.handle.net/10072/12322
dc.description.abstractIn this paper, we report on the recognition accuracy of the multiframe GMM-based block quantiser for the coding of MFCC features in a distributed speech recognition framework under varying noise conditions. All experiments were performed using the ETSI Aurora-2 connected-digits recognition task. For comparison, we have also investigated other quantisation schemes such as the memoryless GMM-based block quantiser, the unconstrained vector quantiser, and non-uniform scalar quantisers. The results show that the rate-distortion efficiency of the quantiser is a factor in determining the level of recognition accuracy at low to medium levels of additive noise. For high levels of additive noise, the influence of rate-distortion efficiency diminishes and the recognition accuracy becomes dependent on the recognition features.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent106488 bytes
dc.format.extent44 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.languageEnglish
dc.language.isoeng
dc.publisherIEEE Signal Processing Society
dc.publisher.placeNew Jersey, USA
dc.publisher.urihttp://ieeexplore.ieee.org/servlet/opac?punumber=11024
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofconferencename31st IEEE International Conference on Acoustics, Speech and Signal Processing
dc.relation.ispartofconferencetitle2006 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-13
dc.relation.ispartofdatefrom2006-05-14
dc.relation.ispartofdateto2006-05-19
dc.relation.ispartoflocationToulouse, FRANCE
dc.relation.ispartofpagefrom189
dc.relation.ispartofpagefrom4 pages
dc.relation.ispartofpageto192
dc.relation.ispartofpageto4 pages
dc.relation.ispartofvolume1
dc.rights.retentionY
dc.subject.fieldofresearchcode280206
dc.subject.fieldofresearchcode280204
dc.titleMulti-frame GMM-based block quantisation for distributed speech recognition under noisy conditions
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, Griffith School of Engineering
gro.rights.copyright© 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.
gro.date.issued2006
gro.hasfulltextFull Text
gro.griffith.authorPaliwal, Kuldip K.
gro.griffith.authorSo, Stephen


Files in this item

This item appears in the following Collection(s)

  • Conference outputs
    Contains papers delivered by Griffith authors at national and international conferences.

Show simple item record