Evaluating the Effect of Gesture and Language on Personality Perception in Conversational Agents

A significant goal in multi-modal virtual agent research is to determine how to vary expressive qualities of a character so that it is perceived in a desired way. The "Big Five" model of personality offers a potential framework for organizing these expressive variations. In this work, we focus on one parameter in this model - extraversion - and demonstrate how both verbal and non-verbal factors impact its perception. Relevant findings from the psychology literature are summarized. Based on these, an experiment was conducted with a virtual agent that demonstrates how language generation, gesture rate and a set of movement performance parameters can be varied to increase or decrease the perceived extraversion. Each of these factors was shown to be significant. These results offer guidance to agent designers on how best to create specific characters.