Spoken dialog strategy based on understanding graph search

We regarded information retrieval as a graph search problem and proposed several novel dialog strategies that can recover from misrecognition through a spoken dialog that traverses the graph. To recover from misrecognition without seeking confirmation, our system kept multiple understanding hypotheses at each turn and searched for a globally optimal hypothesis in the graph whose nodes express understanding states across user utterances in a whole dialog. As for a dialog strategy, we introduced a new criterion based on efficiency in information retrieval and consistency with understanding hypotheses to select an appropriate system response. Using such criterion, the system removes the ambiguity so that users do not feel that a response that conflicts with the actual user intent is unnatural. We developed a spoken dialog system using these techniques and showed dialog examples in which misrecognition was naturally corrected. We also showed that our strategy was efficient in terms of the number of turns.