Discriminative Hidden Markov Models Using Vector-Valued Dynamic Weighting Parameters for Alphabet Recognition

In this work, an integrated approach to vector dynamic feature extraction is described in the design of a hidden Markov model (VVD-IHMM) based speech recognizer. The new model contains state-dependent, vector-valued weighting functions responsible for transforming static speech features into the dynamic ones. In this paper, the minimum classification error (MCE) is extended from the earlier formulation of VVD-IHMM that applies to a novel maximum-likelihood based training algorithm. The experimental results on alphabet classification demonstrate the effectiveness of the MCS-trained new model relative to VVD-IHMM using dynamic features that have been subject to optimization during MLE-training.