Stochastic segment modelling using the estimate-maximize algorithm (speech recognition)

Abstract

A probabilistic model called the stochastic segment model is introduced that describes the statistical dependence of all the frames of a speech segment. The model uses a time-warping transformation to map the sequence of observed frames to the appropriate frames of the segment model. The joint density of the observed frames is then given by the joint density of the selected model frames. The automatic training and recognition algorithms are discussed and a few preliminary recognition results are presented.<>