Spoken Dialogue for Simulation Control and Conversational Tutoring

摘要

This demonstration shows a flexible tutoring system for studying the effects of different tutoring strategies enhanced by a spoken language interface. The hypothesis is that spoken language increases the effectiveness of automated tutoring. The focus is on the SCoT-DC spoken language tutor for Navy damage control. However, because SCoT-DC performs reflective tutoring on DC-TRAIN simulator sessions, the authors also have developed a speech interface for the existing DC-TRAIN damage control simulator to promote ease of use as well as consistency of interface. The tutor is developed within the Architecture for Conversational Intelligence. They use Open Agent Architecture (OAA) for communication between agents based on the Nuance speech recognizer, the Gemini natural language system, and Festival speech synthesis. The tutor adds its own dialog manager agent for general principles of conversational intelligence, and a tutor agent, which uses tutoring strategies and tactics to plan out an appropriate review and react to the student's answers to questions and desired topics. The SCoT-DC tutor, in Socratic style, asks questions rather than giving explanations. The tutor has a repertoire of hinting tactics to deploy in response to student answers to questions, and it identifies and discusses repeated mistakes. The student is able to ask 'why' questions after certain tutor explanations, and to alter the tutorial plan by requesting that the tutor skip discussion of certain topics. In DC-TRAIN, the system uses several windows to provide information graphically, in addition to the spoken messages. In SCoT-DC, the Ship Display from DC-TRAIN is used for both multimodal input and output. Both DC-TRAIN and SCoT-DC use the same overall Gemini grammar. In a Nuance language model compiled from the Gemini grammar, different top-level grammars are used in SCoT-DC to enhance speech recognition based on expected answers.