Contextual constraints based on dialogue models in database search task for spoken dialogue systems

This paper describes the incorporation of contextual information into spoken dialogue systems in the database search task. Appropriate dialogue modeling is required to manage automatic speech recognition (ASR) errors using dialogue-level information. We define two dialogue models: a model for dialogue flow and a model of structured dialogue history. The model for dialogue flow assumes dialogues in the database search task consist of only two modes. In the structured dialogue history model, query conditions are maintained as a tree structure, taking into consideration their inputted order. The constraints derived from these models are integrated by using a decision tree learning, so that the system can determine a dialogue act of the utterance and whether each content word should be accepted or rejected, even when it contains ASR errors. The experimental result showed that our method could interpret content words better than conventional one without the contextual information. Furthermore, it was also shown that our method was domain-independent because it achieved equivalent accuracy in another domain with-out any more training.