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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chang-wen Hsu Lin-shan Lee |
| Copyright Year | 2004 |
| Description | Author affiliation: Graduate Inst. of Commun. Eng., Nat. Taiwan Univ., Taiwan (Chang-wen Hsu; Lin-shan Lee) |
| Abstract | Cepstral mean subtraction (CMS) and cepstral normalization (CN) have been popularly used to normalize the first and the second moments of cepstral coefficients, and proved to be very helpful for robust speech recognition (Furui, S. 1981; Viikki, O. and Laurila, K., 1998). A unified formulation for higher order cepstral moment normalization (HOCMN) is developed by extending the concept of CMS and CN to orders much higher than three. A whole family of normalization techniques for different orders is thus proposed. Preliminary experimental results based on Aurora 2.0 showed that the recognition accuracy can be significantly improved with this approach under all noisy conditions. For example, HOCMN/sub (1,5,100)/ (normalization of the first, fifth and 100th order cepstral moments) is shown to offer an error rate reduction of 32.83% as compared to the conventional CN with a full-utterance processing interval, or an error rate reduction of 20.78% as compared to CN with a segmental processing interval. |
| File Size | 284819 |
| File Format | |
| ISBN | 0780384849 |
| ISSN | 15206149 |
| DOI | 10.1109/ICASSP.2004.1325956 |
| 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 | Cepstral analysis Robustness Speech recognition Mel frequency cepstral coefficient Collision mitigation Error analysis Low-frequency noise Neural networks Probability density function Noise reduction |
| Content Type | Text |
| Resource Type | Article |
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