Because of the great variability of factors to take into account, designing a spoken dialogue system is still a tailoring task. Rapid design and reusability of previous work is made very difficult. For these reasons, the application of machine learning methods to dialogue strategy optimization has become a leading subject of researches this last decade. Yet, techniques such as reinforcement learning are very demanding in training data while obtaining a substantial amount of data in the particular case of spoken dialogues is time-consuming and therefore expansive. In order to expand existing data sets, dialogue simulation techniques are becoming a standard solution. In this paper we describe a user modeling technique for realistic simulation of man-machine goal-directed spoken dialogues. This model, based on a stochastic description of man-machine communication, unlike previously proposed models, is consistent along the interaction according to its history and a predefined user goal.