Leveraging the Dynamics of Non-Verbal Behaviors For Social Attitude Modeling

Abstract

An Embodied Conversational Agent (ECA) is a virtual character designed to interact with humans in the most natural way. In the recent years, ECAs have been deployed in various contexts, such as commercial consulting and social training. In the context of social training, the virtual agent should be able to express different social attitudes in order to train the user in different situations, likely to occur in real life. Previous studies from psychology underlined the importance of considering the non-verbal behavior as well as its evolution over time, for efficient modeling of interpersonal attitudes. Inspired by these works as well as by advances from sequence mining, we propose to model attitude variation as a sequence of non-verbal signals, each being described by its starting time and duration. We demonstrate the efficiency of our model by integrating the sequences representing attitude variation in an ECA and assessing the obtained results based on the interpersonal circumplex, statistical tests and accuracy measures. To the best of our knowledge, this is the first attempt to study the relationship, in term of perception, between different attitude variations.