Analyzing temporal transition of real user's behaviors in a spoken dialogue system

Managing various behaviors of real users is indispensable for spoken dialogue systems to operate adequately in real environments. We have analyzed various users' behaviors using data collected over 34 months from the Kyoto City Bus Information System. We focused on "barge-in" and added barge-in rates to our analysis. Temporal transitions of users' behaviors, such as automatic speech recognition (ASR) accuracy, task success rates and barge-in rates, were initially investigated. We then examined the relationship between ASR accuracy and barge-in rates. Analysis revealed that the ASR accuracy of utterances inputted with barge-ins was lower because many novices, who were not accustomed to the timing when to utter, used the system. We also observed that the ASR accuracy of utterances with barge-ins differed based on the barge-in rates of individual users. The results indicate that the barge-in rate can be used as a novel user profile for detecting ASR errors.