Belief confirmation in spoken dialog systems using confidence measures

The approach proposed is an alternative to the traditional architecture of spoken dialogue systems where the system belief is either not taken into account during the automatic speech recognition process or included in the decoding process but never challenged. By representing all the conceptual structures handled by the dialogue manager by finite state machines and by building a conceptual model that contains all the possible interpretations of a given word-graph, we propose a decoding architecture that searches first for the best conceptual interpretation before looking for the best string of words. Once both N-best sets (at the concept level and at the word level) are generated, a verification process is performed on each N-best set using acoustic and linguistic confidence measures. A first selection strategy that does not include for the moment the dialogue context is proposed and significant error reduction on the understanding measures are obtained.