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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Xiao-Bing Li Jin-Yu Li Ren-Hua Wang |
| Copyright Year | 2004 |
| Description | Author affiliation: USTC iFly Speech Lab, Univ. of Sci. & Technol. of China, Anhui, China (Xiao-Bing Li; Jin-Yu Li; Ren-Hua Wang) |
| Abstract | In this paper, the minimum classification error (MCE) method is extended to optimize both linear discriminant analysis (LDA) transformation and the classification parameters for dimensionality reduction. Firstly, under the HMM-based continuous speech recognition (CSR) framework, we use the MCE criterion to optimize the conventional dimensionality reduction method, which uses LDA to transform the standard MFCC. Then, a new dimensionality reduction method is proposed. In the new method, the combination of discrete cosine transform (DCT) and LDA, as used in the conventional method, is replaced by a single LDA transformation, which is optimized according to MCE criterion along with the classification parameters. Experimental results on TiDigits show that even when the feature dimension is reduced to 14, the performance of this new method is as good as that of the MCE-trained system using 39 dimension MFCC. It also outperforms our MCE-optimized conventional dimensionality reduction method. |
| File Size | 263273 |
| File Format | |
| ISBN | 0780384849 |
| ISSN | 15206149 |
| DOI | 10.1109/ICASSP.2004.1325941 |
| 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 | Linear discriminant analysis Discrete cosine transforms Hidden Markov models Speech recognition Decorrelation Optimization methods Feature extraction Scattering Speech analysis Discrete transforms |
| Content Type | Text |
| Resource Type | Article |
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