Application of a modified neural fuzzy network and an improved genetic algorithm to speech recognition

Leung, KF; Leung, FHF; Lam, HK; Ling, SH

Application of a modified neural fuzzy network and an improved genetic algorithm to speech recognition

Leung, KF Leung, FHF Lam, HK Ling, SH

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Publication Type:: Journal Article
Citation:: Neural Computing and Applications, 2007, 16 (4-5), pp. 419 - 431
Issue Date:: 2007-05-01

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Field	Value	Language
dc.contributor.author	Leung, KF	en_US
dc.contributor.author	Leung, FHF	en_US
dc.contributor.author	Lam, HK	en_US
dc.contributor.author	Ling, SH https://orcid.org/0000-0003-0849-5098	en_US
dc.date.issued	2007-05-01	en_US
dc.identifier.citation	Neural Computing and Applications, 2007, 16 (4-5), pp. 419 - 431	en_US
dc.identifier.issn	0941-0643	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15121
dc.description.abstract	This paper presents the recognition of speech commands using a modified neural fuzzy network (NFN). By introducing associative memory (the tuner NFN) into the classification process (the classifier NFN), the network parameters could be made adaptive to changing input data. Then, the search space of the classification network could be enlarged by a single network. To train the parameters of the modified NFN, an improved genetic algorithm is proposed. As an application example, the proposed speech recognition approach is implemented in an eBook experimentally to illustrate the design and its merits. © Springer-Verlag London Limited 2007.	en_US
dc.relation.ispartof	Neural Computing and Applications	en_US
dc.relation.isbasedon	10.1007/s00521-006-0068-4	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Application of a modified neural fuzzy network and an improved genetic algorithm to speech recognition	en_US
dc.type	Journal Article
utslib.citation.volume	4-5	en_US
utslib.citation.volume	16	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1702 Cognitive Sciences	en_US
dc.location.activity	WOS:000247428700009	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.issue	4-5	en_US
pubs.publication-status	Published	en_US
pubs.volume	16	en_US

Abstract:

This paper presents the recognition of speech commands using a modified neural fuzzy network (NFN). By introducing associative memory (the tuner NFN) into the classification process (the classifier NFN), the network parameters could be made adaptive to changing input data. Then, the search space of the classification network could be enlarged by a single network. To train the parameters of the modified NFN, an improved genetic algorithm is proposed. As an application example, the proposed speech recognition approach is implemented in an eBook experimentally to illustrate the design and its merits. © Springer-Verlag London Limited 2007.

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http://hdl.handle.net/10453/15121