Flexible Spoken Dialogue System based on User Models and Dynamic Generation of VoiceXML Scripts

We realize a telephone-based collaborative natural language dialogue system. Since natural language involves very various expressions, a large number of VoiceXML scripts need to be prepared to handle all possible input patterns. We realize flexible dialogue management for various user utterances by generating VoiceXML scripts dynamically. Moreover, we address appropriate user modeling in order to generate cooperative responses to each user. Specifically, we set up three dimensions of user models: skill level to the system, knowledge level on the target domain and the degree of hastiness. The models are automatically derived by decision tree learning using real dialogue data collected by the system. Experimental evaluation shows that the cooperative responses adapted to individual users serve as good guidance for novice users without increasing the dialogue duration for skilled users.