A spoken dialog system with automatic recovery mechanism from misrecognition

We proposed a novel dialog strategy which can recover from mis- recognition through a spoken dialog. To recover from the misrecog- nition without confirmation, our system kept multiple understanding hypotheses at each turn and ´searched´ for a globally optimal hypothesis across user´s utterances in a whole dialog. As for a dialog strategy, we introduced a new criterion based on ´efficiency for convergence´ and ´consistency with understanding hypotheses´ to select an appropriate system response. Using such criterion, the system removes the ambiguity without making the user feel unnatural in relation to the response conflicting with actual user intent. We also proposed to adopt the repetition utterance detection to update the understanding hypotheses. We developed a spoken dialog system using these techniques and showed some dialog examples in which misrecognition was naturally corrected. We also showed that our strategy was efficient in terms of the number of turns.