The automatic speech understanding problem could be considered as an association problem between two different languages. At the entry, the request expressed in natural language and at the end, just before the interpretation stage, the same request is expressed in term 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 network based method to automatically extract the underlined concepts. We also propose a new approach for the vector representation of words. We finish this paper by a description of the postprocessing step during which, we label our sentences and we generate the corresponding SQL queries. This step allows us to validate our speech understanding approach by obtaining good results. In fact, a rate of 92.5% of well formed SQL requests has been achieved on the test corpus.