The need of robust parsers is more and more essential as spoken human-machine communication meets an impressive development. Because of its uncontrolled nature, spontaneous speech presents indeed a high rate of extragrammatical constructions (hesitations, repetitions, self-corrections, etc.). As a result, spontaneous speech rapidly catches out most syntactic parsers, in spite of the frequent addition of some corrective methods [7]. Therefore, most dialog systems restrict the linguistic analysis of the spoken utterances to a simple extraction of keywords [2]. This selective approach led to significant results in some restricted applications (ATIS), but it does not seem appropriate for higher level tasks, for which the utterances cannot be reduced to a simple set of keywords. As a result, neither the syntactic methods nor the selective approaches can fully satisfy the constraints of robustness and exhaustivity required by the human-machine communication. This paper precisely presents a detailed semantic parser (ALPES) which masters most spoken utterances.