Décodage conceptuel à partir de graphes de mots sur le corpus de dialogue Homme-Machine MEDIA
Christophe Servan, Christian Raymond, Frédéric Béchet, Pascal Nocéra.
Within the framework of the French evaluation program MEDIA on spoken dialogue systems, this paper presents the methods proposed at the LIA for the robust extraction of basic conceptual constituents (or concepts) from an audio message. The conceptual decoding model proposed follows a stochastic paradigm and is directly integrated into the Automatic Speech Recognition (ASR) process. This approach allows us to keep the probabilistic search space on sequences of words produced by the ASR module and to project it to a probabilistic search space of sequences of concepts. The experiments carried on on the MEDIA corpus show that the performance reached by our approach is better than the trdiational sequential approach that looks first for the best sequence of words before looking for the best sequence of concepts.