Bayes Risk-based Dialogue Management for Document Retrieval System with Speech Interface

We propose an efficient dialogue management for an information navigation system based on a document knowledge base with a spoken dialogue interface. In order to perform robustly for fragmental speech input and erroneous output of an automatic speech recognition (ASR), the system should selectively use N-best hypotheses of ASR and contextual information. The system also has several choices in generating responses or confirmations. In this work, we formulate the optimization of the choices based on a unified criterion: Bayes risk, which is defined based on reward for correct information presentation and penalty for redundant turns. We have evaluated this strategy with a spoken dialogue system which also has questionanswering capability. Effectiveness of the proposed framework was confirmed in the success rate of retrieval and the average number of turns.