In this paper, an integrated approach to vector dynamic feature extraction is proposed in the design of a hidden Markov model (HMM) based speech recognizer. The integrated model we developed in this study generalizes the conventional, currently widely used dynamic-parameter technique, which has been confined strictly to the preprocessing domain only, in two significant ways. First, the new model contains state-dependent, vector-valued weighting functions responsible for transforming static speech features into the dynamic ones in a slowly time-varying manner. Second, a novel maximum- likelihood based training algorithm is developed for the model that allows joint optimization of the state-dependent, vector-valued weighting functions and the remaining conventional HMM parameters. The experimental results on alphabet classification demonstrate the effectiveness of the new model relative to standard HMM using dynamic features that have not been subject to optimization during training.