Automatic adjustments of the Markov models topology for speech recognition applications over the telephone

Abstract

This paper presents some automatic adjustments of the structure of Markov models with the objective of either reducing model complexity, or improving recognition performance. These modifications are tested on a 36 word vocabulary recorded by more than 500 speakers over the telephone network. The reduction of the model complexity is carried out by merging the similar gaussian functions using an iterative procedure. A 40% reduction of the number of gaussian functions is obtained on word based models without altering recognition performance.

The improvement of the recognition performance is obtained by dynamically expanding the Markov model. This is achieved mainly by splitting the gaussian functions which make the highest contribution to the observation probability of the training set and by discarding the infrequently used transitions. After some iterations (involving the splitting and discarding operators) a 30% reduction of the word error rate is achieved using pseudo-diphone based models.