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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 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)
In this paper, we present an approach to spoken dialog management based on the use of a Stochastic Finite-State Transducer estimated from a dialog corpus. The states of the Stochastic Finite-State Transducer represent the dialog states, the input alphabet includes all the possible user utterances, without considering specific values, and the set of system(More)
Atros is an automatic speech recognition/understanding/translation system whose knowledge sources (acoustic models, lexical models, syntactic language models, semantic models and translation models) can be learnt automatically from training data by using similar techniques. The search process in Atros is performed through a Synchronous Beam Search(More)
In this paper, we present a statistical approach for the automatic generation of dialogs by means of a user simulator. This technique can be used to generate dialogs with reduced effort, facilitating the evaluation and improvement of spoken dialog systems. In our approach for user simulation, the user answer is selected taking into account the history of(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)
In this paper, we present an approach for automatically acquiring a dialog corpus by means of the interaction of a dialog manager and a user simulator. A random selection of the answers has been used for the operation of both modules, defining stop conditions for automatically deciding if the dialog is successful or not. Therefore, an initial corpus is not(More)