Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1389
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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorLeung, KF-
dc.creatorLeung, FHF-
dc.creatorLam, HK-
dc.creatorTam, PKS-
dc.date.accessioned2014-12-11T08:26:23Z-
dc.date.available2014-12-11T08:26:23Z-
dc.identifier.isbn0-7803-7810-5-
dc.identifier.urihttp://hdl.handle.net/10397/1389-
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rights© 2003 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_US
dc.rightsThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.en_US
dc.subjectGenetic algorithmsen_US
dc.subjectMathematical modelsen_US
dc.subjectNeural networksen_US
dc.subjectSpeech recognitionen_US
dc.titleNeural fuzzy network and genetic algorithm approach for Cantonese speech command recognitionen_US
dc.typeConference Paperen_US
dc.description.otherinformationAuthor name used in this publication: K. F. Leungen_US
dc.description.otherinformationAuthor name used in this publication: F. H. F. Leungen_US
dc.description.otherinformationAuthor name used in this publication: P. K. S. Tamen_US
dc.description.otherinformationCentre for Multimedia Signal Processing, Department of Electronic and Information Engineeringen_US
dc.description.otherinformationRefereed conference paperen_US
dcterms.abstractThis paper presents the recognition of Cantonese speech commands using a proposed neural fuzzy network with rule switches. By introducing a switch to each rule, the optimal number of rules can be learned. An improved genetic algorithm (GA) is proposed to train the parameters of the membership functions and the optimal rule set for the proposed neural fuzzy network. An application example of Cantonese command recognition in electronic books will be given to illustrate the merits of the proposed approach.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationFUZZ-IEEE 2003 : proceedings of the 12th IEEE International Conference on Fuzzy Systems : Sunday 25 May-Wednesday 28 May, 2003, St. Louis, Missouri, USA, p. 208-213-
dcterms.issued2003-
dc.identifier.isiWOS:000183448800036-
dc.identifier.scopus2-s2.0-0038199520-
dc.relation.ispartofbookFUZZ-IEEE 2003 : proceedings of the 12th IEEE International Conference on Fuzzy Systems : Sunday 25 May-Wednesday 28 May, 2003, St. Louis, Missouri, USA-
dc.relation.conferenceIEEE International Conference on Fuzzy Systems [FUZZ]-
dc.identifier.rosgroupidr11474-
dc.description.ros2002-2003 > Academic research: refereed > Refereed conference paper-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryVoR alloweden_US
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