In a spoken dialogue system, the dialogue manager needs to make decisions in a highly noisy environment, mainly due to speech recognition and understanding errors. This work addresses this issue by proposing a framework to interface efficient probabilistic modeling for both the spoken language understanding module and the dialogue management module. First hierarchical semantic frames are inferred and composed so as to build a thorough representation of the userís utterance semantics. Then this representation is mapped into a feature-based summary space in which is defined the set of dialogue states used by the stochastic dialogue manager, based on the partially observable Markov decision process (POMDP) paradigm. This allows a planning of the dialogue course taking into account the uncertainty on the current dialogue state and tractability is ensured by the use of an intermediate summary space.
A preliminary implementation of such a system is presented on the Media domain. The task is touristic information and hotel booking, and the availability of WoZ data allows to consider a model-based approach to the POMDP dialogue manager.
Bibliographic reference. Pinault, Florian / Lefèvre, Fabrice / De Mori, Renato (2009): "Feature-based summary space for stochastic dialogue modeling with hierarchical semantic frames", In INTERSPEECH-2009, 284-287.