Multichannel filters for speech recognition using a particle swarm optimization
|dc.contributor.author||Chan, Kit Yan|
|dc.contributor.author||Yiu, Ka Fai|
|dc.identifier.citation||Chan, Kit Yan and Yiu, Cedric K.F. and Nordholm, Sven. 2012. Multichannel filters for speech recognition using a particle swarm optimization, in Proceedings of the 2012 12th IEEE International Conference on Control Automation Robotics and Vision (ICARCV 2012), Dec 5-7 2012. Guangzhou, China: Control Systems Society.|
Speech recognition has been used in various real-world applications such as automotive control, electronic toys, electronic appliances etc. In many applications involved speech control functions, a commercial speech recognizer is used to identify the speech commands voiced out by the users and the recognized command is used to perform appropriate operations. However, users’ commands are often corrupted by surrounding ambient noise. It decreases the effectiveness of speech recognition in order to implement the commands accurately. This paper proposes a multichannel filter to enhance noisy speech commands, in order to improve accuracy of commercial speech recognizers which work under noisy environment. An innovative particle swarm optimization (PSO) is proposed to optimize the parameters of the multichannel filter which intends to improve accuracy of the commercial speech recognizer working under noisy environment. The effectiveness of the multichannel filter was evaluated by interacting with a commercial speech recognizer, which was worked in a warehouse.
|dc.title||Multichannel filters for speech recognition using a particle swarm optimization|
|dcterms.source.title||Proceedings of the 12th IEEE International Conference on Control, Automation, Robotics and Vision|
|dcterms.source.series||Proceedings of the 12th IEEE International Conference on Control, Automation, Robotics and Vision|
|dcterms.source.conference||The 12th IEEE International Conference on Control, Automation, Robotics and Vision|
|dcterms.source.conference-start-date||Dec 5 2012|
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