Enhancement of speech recognitions for control automation using an intelligent particle swarm optimization

Chan, KY; Yiu, CKF; Dillon, TS; Nordholm, S; Ling, SH

Enhancement of speech recognitions for control automation using an intelligent particle swarm optimization

Chan, KY Yiu, CKF Dillon, TS Nordholm, S Ling, SH

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Industrial Informatics, 2012, 8 (4), pp. 869 - 879
Issue Date:: 2012-11-01

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Field	Value	Language
dc.contributor.author	Chan, KY	en_US
dc.contributor.author	Yiu, CKF	en_US
dc.contributor.author	Dillon, TS	en_US
dc.contributor.author	Nordholm, S	en_US
dc.contributor.author	Ling, SH https://orcid.org/0000-0003-0849-5098	en_US
dc.date.issued	2012-11-01	en_US
dc.identifier.citation	IEEE Transactions on Industrial Informatics, 2012, 8 (4), pp. 869 - 879	en_US
dc.identifier.issn	1551-3203	en_US
dc.identifier.uri	http://hdl.handle.net/10453/23087
dc.description.abstract	For over two decades, speech control mechanisms have been widely applied in manufacturing systems such as factory automation, warehouse automation, and industrial robotic control for over two decades. To implement speech controls, a commercial speech recognizer is used as the interface between users and the automation system. However, users' commands are often contaminated by environmental noise which degrades the performance of speech recognition for controlling automation systems. This paper presents a multichannel signal enhancement methodology to improve the performance of commercial speech recognizers. The proposed methodology aims to optimize speech recognition accuracy of a commercial speech recognizer in a noisy environment based on a beamformer, which is developed by an intelligent particle swarm optimization. It overcomes the limitation of the existing signal enhancement approaches whereby the parameters inside commercial speech recognizers are required to be tuned, which is impossible in a real-world situation. Also, it overcomes the limitation of the existing optimization algorithm including gradient descent methods, genetic algorithms and classical particle swarm optimization that are unlikely to develop optimal beamformers for maximizing speech recognition accuracy. The performance of the proposed methodology was evaluated by developing beamformers for a commercial speech recognizer, which was implemented on warehouse automation. Results indicate a significant improvement regarding speech recognition accuracy. © 2012 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Industrial Informatics	en_US
dc.relation.isbasedon	10.1109/TII.2012.2187910	en_US
dc.subject.classification	Electrical & Electronic Engineering	en_US
dc.title	Enhancement of speech recognitions for control automation using an intelligent particle swarm optimization	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	8	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	10 Technology	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/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	8	en_US

Abstract:

For over two decades, speech control mechanisms have been widely applied in manufacturing systems such as factory automation, warehouse automation, and industrial robotic control for over two decades. To implement speech controls, a commercial speech recognizer is used as the interface between users and the automation system. However, users' commands are often contaminated by environmental noise which degrades the performance of speech recognition for controlling automation systems. This paper presents a multichannel signal enhancement methodology to improve the performance of commercial speech recognizers. The proposed methodology aims to optimize speech recognition accuracy of a commercial speech recognizer in a noisy environment based on a beamformer, which is developed by an intelligent particle swarm optimization. It overcomes the limitation of the existing signal enhancement approaches whereby the parameters inside commercial speech recognizers are required to be tuned, which is impossible in a real-world situation. Also, it overcomes the limitation of the existing optimization algorithm including gradient descent methods, genetic algorithms and classical particle swarm optimization that are unlikely to develop optimal beamformers for maximizing speech recognition accuracy. The performance of the proposed methodology was evaluated by developing beamformers for a commercial speech recognizer, which was implemented on warehouse automation. Results indicate a significant improvement regarding speech recognition accuracy. © 2012 IEEE.

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http://hdl.handle.net/10453/23087