Décodage conceptuel à partir de graphes de mots sur le corpus de dialogue Homme-Machine MEDIAReport as inadecuate




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1 LIA - Laboratoire Informatique d-Avignon

Abstract : 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 traditional sequential approach that looks first for the best sequence of words before looking for the best sequence of concepts.

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Keywords : spoken language understanding dialogue systems Automatic Speech Recognition

Mots-clés : compréhension de la parole reconnaissance de la parole dialogue homme-machine





Author: Christophe Servan - Christian Raymond - Frédéric Béchet - Pascal Nocera -

Source: https://hal.archives-ouvertes.fr/



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