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In this article, we present an approach to the development of a stochastic dialog manager. The model used by this dialog manager to generate its turns takes into account both the last turns of the user and system, and the information supplied by the user throughout the dialog. As the space of situations that can be presented in the dialogs is too large,(More)
Current speech technology allows us to build efficient speech recognition systems. However, model learning of knowledge sources in a speech recognition system is not a closed problem. In addition, lower demand of computational requirements are crucial to building real-time systems. ATROS is an automatic speech recognition system whose acoustic , lexical,(More)
One of the main problems in Natural Language Processing is lexical ambiguity, words often have multiple lexical functionalities (i.e. they can have various parts-of-speech) or have several semantic meanings. Nowadays, the semantic ambiguity problem, most known as Word Sense Disambiguation, is still an open problem in this area. The accuracy of the different(More)
In this paper, we present a statistical approach for the development of a dialog manager and for learning optimal dialog strategies. This methodology is based on a classification procedure that considers all of the previous history of the dialog to select the next system answer. To evaluate the performance of the dialog system, the statistical approach for(More)
In this work, we present an approach to take advantage of confidence measures obtained during the recognition and understanding processes of a dialog system, in order to guide the behavior of the dialog manager. Our approach allows the system to ask the user for confirmation about the data which have low confidence values associated to them, after the(More)