HMM speech recognizer based on discriminative metric design

We apply discriminative metric design (DMD), the general methodology of discriminative class-feature design, to a speech recognizer using a hidden Markov model (HMM) classification. This implementation enables one to represent the salient feature of each acoustic unit that is essential for recognition decision, and accordingly enhances robustness against irrelevant pattern variations. We demonstrate its high utility by experiments of speaker-dependent Japanese word recognition using linear feature extractors and mixture Gaussian HMMs. Furthermore, we summarize several other proposed design methods related to our DMD and show that they are special implementations of the DMD concept.