Understanding Speech Based on a Bayesian Concept Extraction Method

Abstract

The automatic speech understanding problem could be considered as an association problem between two different languages. At the entry, the query expressed in oral or written natural language and at the end, just before the interpretation stage, the same request is expressed in term of concepts. One concept represents a given meaning, it is defined by a set of words sharing the same semantic properties. In this paper, we propose a new Bayesian network based method to automatically extract the underlined concepts. We also propose three different approaches for the vector representation of words. This representation allows the Bayesian network to build the adequate list of concepts for the considered application. This step is very important to obtain well built concepts. We finish this paper by a description of the post-processing step during which, we label our sentences and we generate the corresponding SQL queries. This step allows us to validate our automatic understanding approach and to obtain 92.5% of correct SQL queries on the test corpus.