Development of a speech understanding system

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The objective of this research is development of fundamental techniques necessary to understanding spoken dialogue, which include knowledge-based speech recognition system, non-monotonic reasoning in natural language processing, and dialogue modeling. The following are the summary of the research results.1) We verified the efficiency of the knowledge-based approach for Korean speech recognition. Furthermore, some new ideas were proposed to improve the speech recognition. To avoid the difficulties in segmentation, a non-uniform unit is introduced. Every unit has its stationary point at each end of the unit, and transient part in the middle. The parameter trajectory is described by symbolic representation and fuzzy linguistic variables. Redundancy of speech data is used to improve the performance of the recognition system in the post-processor. The prototype system was tested with continuous Korean digit speech of unknown length, and the recognition rate of 97% was obtained.2) Understanding of continuous speech is generally a tough problem, since acoustic information is unreliable. An efficient search mechanism is indispensable because the combination of ambiguous information is very large. Then, we developed a framework of speech understanding system based on ATMS, which is a method of non-monotonic reasoning. The introduction of ATMS reduced elapsed time of natural language processing from 64 sec to 45 sec for understanding speech of 8 Japanese sentences.3) Two kinds of dialogue model characterizing structures in dialogue were proposed for understanding spoken dialogue. One is the SR-plan model which describes utterance pairs composed of the stimulus and the response. The other is Topic Packet Network (TPN) and corresponds to the discourse segments. A mechanism for predicting the next utterance was also developed based on these dialogue models and evaluated on some sample dialogues.