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dc.contributor.authorSo, Stephenen_US
dc.contributor.authorPaliwal, Kuldipen_US
dc.contributor.editorTan, Zheng-Hua; Lindberg, Børgeen_US
dc.date.accessioned2017-04-24T10:06:43Z
dc.date.available2017-04-24T10:06:43Z
dc.date.issued2008en_US
dc.date.modified2011-05-13T06:54:58Z
dc.identifier.isbn9781848001428en_US
dc.identifier.doi10.1007/978-1-84800-143-5_7en_AU
dc.identifier.urihttp://hdl.handle.net/10072/21968
dc.description.abstractIn this chapter, we describe various schemes for quantizing speech features to be used in distributed speech recognition (DSR) systems. We analyze the statistical properties of Mel frequency-warped cepstral coefficients (MFCCs) that are most relevant to quantization, namely the correlation and probability density function shape, in order to determine the type of quantization scheme that would be most suitable for quantizing them efficiently. We also determine empirically the relationship between mean squared error and recognition accuracy in order to verify that quantization schemes, which minimize mean squared error, are also guaranteed to improve the recognition performance. Furthermore, we highlight the importance of noise robustness in DSR and describe the use of a perceptually weighted distance measure to enhance spectral peaks in vector quantization. Finally, we present some experimental results on the quantization schemes in a DSR framework and compare their relative recognition performances.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherSpringeren_US
dc.publisher.placeLondonen_US
dc.publisher.urihttp://www.springerlink.com/en_AU
dc.relation.ispartofbooktitleAutomatic Speech Recognition on Mobile Devices and over Communication Networksen_US
dc.relation.ispartofchapter7en_US
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofpagefrom131en_US
dc.relation.ispartofpageto161en_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchcode280206en_US
dc.subject.fieldofresearchcode280204en_US
dc.titleQuantization of Speech Features: Source Codingen_US
dc.typeBook chapteren_US
dc.type.descriptionB1 - Book Chapters (HERDC)en_US
dc.type.codeB - Book Chaptersen_US
gro.facultyGriffith Sciences, Griffith School of Engineeringen_US
gro.date.issued2008
gro.hasfulltextNo Full Text


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