Use of generalized dynamic feature parameters for speech recognition: maximum likelihood and minimum classification error approaches

In this study, a new hidden Markov model that integrates generalized dynamic feature parameters into the model structure is developed and evaluated using maximum-likelihood (ML) and minimum-classification-error (MCE) pattern recognition approaches. In addition to the motivation of direct minimization of error rate, the MCE approach automatically eliminates the necessity of artificial constraints, which were essential for the model formulation based on the ML approach, on the weighting functions in the definition of the generalized dynamic parameters. We design the loss function for minimizing error rate specifically for the new model, and derive an analytical form of the gradient of the loss function that enables the implementation of the MCE approach. The convergence property of the training procedure based on the MCE approach is investigated, and the experimental results from a standard TIMIT phonetic classification task demonstrate a 13.4% error rate reduction compared with the ML approach.