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Méthodes statistiques pour la compréhension automatique de la parole

Abstract : The work presented in this manuscript aims to carry out an understanding system for the spontaneous speech. We are interested in specific domain systems that concern the oral interrogation of data bases. Our work is based on statistical approach which considers the understanding problem as a translation process between words and semantic concepts. The idea we defend in this thesis is the possibility to obtain significant semantic concepts using clustering methods. We start by defining some semantic measures to quantify the semantic relations between words. Then, we use triggers to build up concepts in an automatic way. In order to improve we test two well known methods : the K-means algorithm and the Kohonen maps. We also propose the use of the Oja and Sanger neural networks. The latter proved to be ineffective in our case. Lastly, we use a Bayesian network conceived for clustering and called AutoClass. AutoClass provides clear and significant concepts.
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Submitted on : Thursday, March 29, 2018 - 10:44:20 AM
Last modification on : Saturday, October 16, 2021 - 11:26:08 AM

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Salma Jamoussi. Méthodes statistiques pour la compréhension automatique de la parole. Autre [cs.OH]. Université Henri Poincaré - Nancy 1, 2004. Français. ⟨NNT : 2004NAN10170⟩. ⟨tel-01746760⟩



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