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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wooil Kim Ohil Kwon Hanseok Ko |
| Copyright Year | 2004 |
| Description | Author affiliation: Dept. of Electron. & Comput. Eng., Korea Univ., Seoul, South Korea (Wooil Kim) |
| Abstract | In this paper, we propose an effective feature compensation scheme based on the speech model in order to achieve robust speech recognition. The proposed feature compensation method is based on parallel combined mixture model (PCMM). The previous PCMM works require a highly sophisticated procedure for estimation of the combined mixture model in order to reflect the time-varying noisy conditions at every utterance. The proposed schemes can cope with the time-varying background noise by employing the interpolation method of the multiple mixture models. We apply the 'data-driven' method to PCMM for more reliable model combination and introduce a frame-synched version for estimation of environments a posteriori. In order to reduce the computational complexity due to multiple models, we propose a technique for mixture sharing. The statistically similar Gaussian components are selected and the smoothed versions are generated for sharing. The performance was examined over Aurora 2.0 and speech corpus recorded while car-driving. The experimental results indicate that the proposed schemes are effective in realizing robust speech recognition and reducing the computational complexities under both simulated environments and real-life conditions. |
| File Size | 247660 |
| File Format | |
| ISBN | 0780384849 |
| ISSN | 15206149 |
| DOI | 10.1109/ICASSP.2004.1326154 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2004-05-17 |
| Publisher Place | Canada |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Interpolation Working environment noise Speech recognition Cepstral analysis Background noise Computational complexity Degradation Noise generators Maximum likelihood linear regression Spatial databases |
| Content Type | Text |
| Resource Type | Article |
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