On the Hidden Markov Model and Dynamic Time Warping for Speech Recognition-A Unified View

This paper gives a unified theoretical view of the Dynamic Time Warping (DTW) and the Hidden Markov Model (HMM) techniques for speech recognition problems. The application of hidden Markov models in speech recognition is discussed. We show that the conventional dynamic time-warping algorithm with Linear Predictive (LP) signal modeling and distortion measurements can be formulated in a strictly statistical framework. It is further shown that the DTW/LP method is implicitly associated with a specific class of Markov models and is equivalent to the probability maximization procedures for Gaussian autoregressive multivariate probabilistic functions of the underlying Markov model. This unified view offers insights into the effectiveness of the probabilistic models in speech recognition applications.