Hidden Markov Models for Understanding in a Dialogue System

Abstract

In this work, we present an approach to Automatic Speech Understanding based on stochastic models. In a first phase, the input sentence is transduced into a sequence of semantic units by using hidden Markov models. In a second phase, a semantic frame is obtained from this sequence of semantic units. We have studied some smoothing techniques in order to take into account the unseen events in the training corpus. We have also explored the possibility of using specific hidden Markov models, depending on the dialogue state. These techniques have been applied to the understanding module of a dialogue system of railway information in Spanish. Some experimental results with written and speech input are presented.