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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Haozheng Li Munteanu, C. |
| Copyright Year | 2010 |
| Description | Author affiliation: National Research Council of Canada (Haozheng Li; Munteanu, C.) |
| Abstract | Increasing the generalization capability of Discriminative Training (DT) of Hidden Markov Models (HMM) has recently gained an increased interest within the speech recognition field. In particular, achieving such increases with only minor modifications to the existing DT method is of significant practical importance. In this paper, we propose a solution for increasing the generalization capability of a widely-used training method - the Minimum Classification Error (MCE) training of HMM - with limited changes to its original framework. For this, we define boundary data - obtained by applying a large steep parameter, and confusion data - obtained by applying a small steep parameter on the training samples, and then do a soft interpolation between these according to the number points of occupancies of boundary data and the number points ratio between the boundary and the confusion occupancies. The final HMM parameters are then tuned in the same manner as in MCE by using the interpolated boundary data. We show that the proposed method achieves lower error rates than a standard HMM training framework on a phoneme classification task for the TIMIT speech corpus. |
| Starting Page | 4906 |
| Ending Page | 4909 |
| File Size | 175130 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424442959 |
| ISSN | 15206149 |
| DOI | 10.1109/ICASSP.2010.5495109 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-03-14 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Hidden Markov models Maximum likelihood estimation Speech recognition Interpolation Testing Councils Error analysis Mutual information Minimum Classification Errors Hidden Markov Model Speech Recognition |
| Content Type | Text |
| Resource Type | Article |
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