For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
An Utterance Prediction Method Based on the Topic Transition Model
Yoichi YAMASHITA Takashi HIRAMATSU Osamu KAKUSHO Riichiro MIZOGUCHI
IEICE TRANSACTIONS on Information and Systems
Publication Date: 1995/06/25
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Issue on Spoken Language Processing)
utterance prediction, topic, dialog, model, spoken dialog understanding,
Full Text: PDF(669.9KB)>>
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.