Stochastic modeling is a flexible method for handling the large variability in speech for recognition applications. In contrast to dynamic time warping where heuristic training methods for estimating word templates are used, stochastic modeling allows a probabilistic and automatic training for estimating models. This paper deals with the improvement of stochastic techniques, especially for a better representation of time varying phenomena.