Comparing ASR modeling methods for spoken dialogue simulation and optimal strategy learning

Speech enabled interfaces are nowadays becoming ubiquitous. The most advanced ones rely on probabilistic pattern matching systems and especially on automatic speech recognition systems. Because of their statistical nature, performances of such systems never reach one hundred percent of correct recognition results. Performances are linked to environmental noise and to intraand inter-speaker variability of course, but also to the acoustical similarities inside the vocabulary of allowed speech entries, which is usually contextual in the case of man-machine dialogue systems. A good dialogue strategy should therefore dynamically handle the potentiality of recognition errors. In this paper, we compare different methods to model ASR systems in the framework of automatic dialogue strategy optimization and we especially emphasize on a contextdependent ASR modeling method.