This paper describes a dialog management system called MASCOTS which manages a dialog between a user and a problem solving system through spoken Japanese and helps the speech understanding system in its language processing. MASCOTS tries to predict the next user utterance based on the architecture for managing dialog with two stacks and plan information. MASCOTS not only contributes to making language processing efficient, but also works for a problem solving system. MASCOTS identifies the kind of the utterance and standardizes its representation form in place of a problem solving system. In this paper, the architecture of MASCOTS is discussed focusing on the characteristics of dialog and two ways of predicting the next user utterance exchanging the information with the language processing system.

This paper describes automatic generation of speech synthesis rules which predict a stress level for each bunsetsu in long noun phrases. The rules are inductively inferred from a lot of speech data by using two kinds of tree-based methods, the conventional decision tree and the SBR-tree methods. The rule sets automatically generated by two methods have almost the same performance and decrease the prediction error to about 14 Hz from 23 Hz of the accent component value. The rate of the correct reproduction of the change for adjacent bunsetsu pairs is also used as a measure for evaluating the generated rule sets and they correctly reproduce the change of about 80%. The effectiveness of the rule sets is verified through the listening test. And, with regard to the comprehensiveness of the generated rules, the rules by the SBR-tree methods are very compact and easy to human experts to interpret and matches the former studies.