Training Bayesian networks for realistic man-machine spoken dialogue simulation

International audienceData collection and annotation are generally required to design or assess spoken dialogue systems. Yet, this is a very time consuming and expensive process. For these reasons, user simulation has become an important trend of research in the field of spoken dialogue systems. The general problem of user simulation is thus to produce as many as necessary natural, various and consistent interactions from as few data as possible. In this paper, we propose a user simulation method based on Bayesian networks (BN) that is able to produce consistent interactions in terms of user goal and dialogue history. The model as been introduced in previous work but parameters were hand-tuned and it was assessed in the framework of automatic learning of optimal dialogue strategies. In this paper, the BN is trained on a database of 1234 human-machine dialogues in the TownInfo domain (a tourist information application). Experiments with a state-of-the-art dialogue system (REALL-DUDE/DIPPER/OAA) have been realized and results in terms of dialog statistics are presented