On the use of matrix derivatives in integrated design of dynamic feature parameters for speech 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 MCE-trained new model relative to VVD-IHMM using dynamic features that have been subject to optimization during MLE-training