A Complete Understanding Speech System Based on Semantic Concepts

In this work, we present a complete speech understanding system based on our speech recognizer: ESPERE. The input signal is processed and the best sentence is then proposed to the understanding module. In our case, the understanding problem is considered as a matching process between two different languages. At the entry, the request expressed in natural language and at the output the corresponding SQL form. The SQL request is obtained after an intermediate step in which the entry is expressed in terms of concepts. A 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 classifier to automatically extract the underlined concepts. We also propose a new approach for vector representation of words. Then, we describe the postprocessing step during which, we label our sentences and we generate the corresponding SQL queries. We conclude our paper by describing the integration step of our understanding module in a complete platform of human-machine oral intercation.