Character expression of a conversational robot for adapting to user personality

Abstract

User adaptation is important in conversational robots to enhance the user experience and engagement. One way of user adaptation is to change the dialogue behavior. A character (e.g. extrovert or agreeable) can be defined to achieve human-like behaviors and user adaptation, and the appropriate character differs depending on each user. In this study, we investigate user adaptation by character expression of conversational robots. Our character expression model adopts the Big Five traits for controlling four dialogue behaviors: amount of utterance, backchannel frequency, filler frequency, and switching pause length. We cluster the system character and user personality into four classes based on an analysis of a speed dating dialogue corpus, and we found the best combinations between the system character and user personality. We implemented the character expression model into a laboratory guide and a chit-chat robot and conducted subjective experiments, where the subjects talked with robots with four different characters and evaluated their impressions of each character. The results shows that the Role model character was preferred for the task-oriented dialogue of the laboratory guide, but a robot character complementary to the subject personality was preferred for the non-task-oriented dialogue of the speed dating and chit-chat.