| Paper: | SLP-P1.1 | ||
| Session: | Feature Extraction and Modeling | ||
| Time: | Tuesday, May 16, 10:30 - 12:30 | ||
| Presentation: | Poster | ||
| Topic: | Speech and Spoken Language Processing: Feature Extraction and Modeling | ||
| Title: | Discriminatively Trained Region Dependent Feature Transforms for Speech Recognition | ||
| Authors: | Bing Zhang, Northeastern University, United States; Spyros Matsoukas, Richard Schwartz, BBN Technologies, United States | ||
| Abstract: | Discriminatively trained feature transforms such as MPE-HLDA, fMPE and MMI-SPLICE have been shown to be effective in reducing recognition errors in today's state-of-the-art speech recognition systems. This paper introduces the concept of Region Dependent Linear Transform (RDLT), which unifies the above three types of feature transforms and provides a framework for the estimation of piece-wise linear feature projections, based on the Minimum Phoneme Error (MPE) criterion. Recognition results on English conversational telephone speech data show that RDLT offers consistent gains over the baseline systems,which are trained using the LDA+MLLT projection. | ||
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