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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Bing Zhang Matsoukas, S. |
| Copyright Year | 2005 |
| Description | Author affiliation: BBN Technol., Cambridge, MA, USA (Bing Zhang) |
| Abstract | We introduce a discriminative feature analysis method that seeks to minimize phoneme errors in lattice-based training frameworks. This technique, referred to as minimum phoneme error heteroscedastic linear discriminant analysis (MPE-HLDA), is shown to be more robust than traditional LDA methods in high dimensional spaces, and easy to incorporate with existing training procedures, such as HLDA-SAT and discriminative training of hidden Markov models (HMMs). Results on conversational telephone speech and broadcast news corpora also show that the recognition accuracy is improved using features selected by MPE-HLDA. |
| Sponsorship | IEEE Signal Processing Soc |
| File Size | 288548 |
| File Format | |
| ISBN | 0780388747 |
| ISSN | 15206149 |
| DOI | 10.1109/ICASSP.2005.1415266 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2005-03-23 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Linear discriminant analysis Speech recognition Speech analysis Hidden Markov models Maximum likelihood estimation Robustness Telephony Broadcasting Control systems Analysis of variance |
| Content Type | Text |
| Resource Type | Article |
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