This paper describes a method for predicting the user's next utterances in spoken dialog based on the topic transition model, named TPN. Some templates are prepared for each utterance pair pattern modeled by SR-plan. They are represented in terms of five kinds of topic-independent constituents in sentences. The topic of an utterance is predicted based on the TPN model and it instantiates the templates. The language processing unit analyzes the speech recognition result using the templates. An experiment shows that the introduction of the TPN model improves the performance of utterance recognition and it drastically reduces the search space of candidates in the input bunsetsu lattice.

A context-sensitive method to predict linguistic expressions in the next utterance in inquiry dialogues is proposed. First, information of the next utterance, the utterance type, the main action and the discourse entities, can be grasped using a dialogue interpretation model. Secondly, focusing in particular on dialogue situations in context, a domain-dependent knowledge-base for literal usage of both noun phrases and verb phrases is developed. Finally, a strategy to make a set of linguistic expressions which are derived from semantic concepts consisting of appropriate expressions can be used to select the correct candidate from the speech recognition output. In this paper, some of the processes are particularly examined in which sets of polite expressions, vocatives, compound nominal phrases, verbal phrases, and intention expressions, which are common in telephone inquiry dialogue, are created.

This paper describes a general interface system for speech input and output and a dialog management system, MASCOTS, which is a component of the interface system. The authors designed this interface system, paying attention to its generality; that is, it is not dependent on the problem-solving system it is connected to. The previous version of MASCOTS dealt with the dialog processing only for the speech input based on the SR-plans. We extend MASCOTS to cover the speech output to the user. The revised version of MASCOTS, named MASCOTS II, makes use of topic information given by the topic packet network (TPN) which models the topic transitions in dialogs. Input and output messages are described with the concept representation based on the case structure. For the speech input, prediction of user's utterance is focused and enhanced by using the TPN. The TPN compensates for the shortages of the SR-plan and improves the accuracy of prediction as to stimulus utterances of the user. As the dialog processing in the speech output, MASCOTS II extracts emphatic words and restores missing words to the output message if necessary, e.g., in order to notify the results of speech recognition. The basic mechanisms of the SR-plan and the TPN are shared between the speech input and output processes in MASCOTS II.