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dc.contributor.authorYiu, Ka Fai
dc.contributor.authorChan, Kit Yan
dc.contributor.authorLow, Siow
dc.contributor.authorNordholm, Sven
dc.date.accessioned2017-01-30T13:34:19Z
dc.date.available2017-01-30T13:34:19Z
dc.date.created2010-03-31T20:02:40Z
dc.date.issued2009
dc.identifier.citationYiu, K. and Chan, K. and Low, S. and Nordholm, S. 2009. A multi-filter system for speech enhancement under low signal-to-noise ratios. Journal of Industrial and Management Optimization. 5 (3): pp. 671-682.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/32975
dc.identifier.doi10.3934/jimo.2009.5.671
dc.description.abstract

In this paper, the problem of deteriorating performance of speech recognition under very low signal-to-noise ratios (SNR) is considered. In particular, for a given pre-trained speech recognizer and for a finite set of speech commands, we show that popular noise reduction methods have a mixed performance in speech recognition accuracy under very low SNR. Although most noise reduction methods are attempting to reduce speech distortion or to increase noise suppression, it does not necessarily improve speech recognition accuracy very much due to the complexity of the recognizer. We propose a new hybrid algorithm to optimize on the speech recognition accuracy directly by mixing different noise reduction methods together. We show that this method can indeed improve the accuracy significantly.

dc.publisherAmerican Institute of Mathematical Sciences
dc.subjectSpeech enhancement
dc.subjectSpeech recognition
dc.subjectNoise reduction
dc.subjectOptimization
dc.titleA multi-filter system for speech enhancement under low signal-to-noise ratios
dc.typeJournal Article
dcterms.source.volume5
dcterms.source.number3
dcterms.source.startPage671
dcterms.source.endPage682
dcterms.source.issn1547-5816
dcterms.source.titleJournal of Industrial and management optimization
curtin.departmentDigital Ecosystems and Business Intelligence Institute (DEBII)
curtin.accessStatusOpen access via publisher
curtin.facultyCurtin Business School
curtin.facultyThe Digital Ecosystems and Business Intelligence Institute (DEBII)


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