Probabilistic Semantic Analysis of Speech

Abstract

This paper presents a new probabilistic approach to semantic analysis of speech. The problem of finding the semantic contents of a word chain is modeled as the problem of assigning semantic attributes to words. The discrete assignment function is characterized by random vectors and its probabilities. By computing the best of all possible statistically modeled assignments, we get the semantic contents of a word chain and along with it a semantic segmentation. The introduced general statistical framework has to deal with incomplete data estimation problems. These are solved applying the Expectation Maximization algorithm. We show that the well-known hidden Markov models result from the suggested theory as a specialization. Experiments prove that this approach works quite well in the domain of train-time-table inquiries for German IC/EC-train connections.