A multi-filter system for speech enhancement under low signal-to-noise ratios
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 perform...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
American Institute of Mathematical Sciences
2009
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/32975 |
| _version_ | 1848753815955701760 |
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| author | Yiu, Ka Fai Chan, Kit Yan Low, Siow Nordholm, Sven |
| author_facet | Yiu, Ka Fai Chan, Kit Yan Low, Siow Nordholm, Sven |
| author_sort | Yiu, Ka Fai |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T08:30:31Z |
| format | Journal Article |
| id | curtin-20.500.11937-32975 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:30:31Z |
| publishDate | 2009 |
| publisher | American Institute of Mathematical Sciences |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-329752017-09-13T16:07:33Z A multi-filter system for speech enhancement under low signal-to-noise ratios Yiu, Ka Fai Chan, Kit Yan Low, Siow Nordholm, Sven Speech enhancement Speech recognition Noise reduction Optimization 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. 2009 Journal Article http://hdl.handle.net/20.500.11937/32975 10.3934/jimo.2009.5.671 American Institute of Mathematical Sciences unknown |
| spellingShingle | Speech enhancement Speech recognition Noise reduction Optimization Yiu, Ka Fai Chan, Kit Yan Low, Siow Nordholm, Sven A multi-filter system for speech enhancement under low signal-to-noise ratios |
| title | A multi-filter system for speech enhancement under low signal-to-noise ratios |
| title_full | A multi-filter system for speech enhancement under low signal-to-noise ratios |
| title_fullStr | A multi-filter system for speech enhancement under low signal-to-noise ratios |
| title_full_unstemmed | A multi-filter system for speech enhancement under low signal-to-noise ratios |
| title_short | A multi-filter system for speech enhancement under low signal-to-noise ratios |
| title_sort | multi-filter system for speech enhancement under low signal-to-noise ratios |
| topic | Speech enhancement Speech recognition Noise reduction Optimization |
| url | http://hdl.handle.net/20.500.11937/32975 |