Abstract
The automatic speech understanding problem could be considered as an association problem between two different languages. At the entry, the query expressed in oral or written natural language and at the end, just before the interpretation stage, the same request is expressed in term of concepts. One 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 three different approaches for the vector representation of words. This representation allows the Bayesian network to build the adequate list of concepts for the considered application. This step is very important to obtain well built concepts. We finish this paper by a description of the post-processing step during which, we label our sentences and we generate the corresponding SQL queries. This step allows us to validate our automatic understanding approach and to obtain 92.5% of correct SQL queries on the test corpus.
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References
Bousquet-Vernhettes, C., Vigouroux, N.: Context use to improve the speech understanding processing. In: Int. Workshop on Speech and Computer, Russia (2001)
Cheeseman, P., Stutz, J.: Bayesian classification (AutoClass): theory and results. Advances in Knowledge Discovery and Data Mining (1996)
Jamoussi, S., Smaïli, K., Haton, J.P.: Neural network and information theory in speech understanding. In: Int. Workshop on Speech and Computer, Russia (2002)
Lefèvre, F., Bonneau-Maynard, H.: Issues in the development of a stochastic speech understanding system. In: Int. Conf. on Spoken Language Processing, Denver (2002)
Pieraccini, R., Levin, E., Vidal, E.: Learning how to understand language. In: Proc. 4rd European Conf. on Speech Communication and Technology, Germany (1993)
Gravier, G., Yvon, F., Jacob, B., Bimbot, F.: Sirocco - un système ouvert de reconnaissance de la parole. Journées d’Etudes sur la Parole, France (2002)
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© 2003 Springer-Verlag Berlin Heidelberg
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Jamoussi, S., Smaïli, K., Haton, JP. (2003). Understanding Speech Based on a Bayesian Concept Extraction Method. In: Matoušek, V., Mautner, P. (eds) Text, Speech and Dialogue. TSD 2003. Lecture Notes in Computer Science(), vol 2807. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39398-6_26
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DOI: https://doi.org/10.1007/978-3-540-39398-6_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20024-6
Online ISBN: 978-3-540-39398-6
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