A listener model: introducing personality traits

Abstract

We present a computational model that generates listening behaviour for a virtual agent. It triggers backchannel signals according to the user's visual and acoustic behaviour. The appropriateness of the backchannel algorithm in a user-agent situation of storytelling, has been evaluated by naive participants, who judged the algorithm-ruled timing of backchannels more positively than a random timing. The system can generate different types of backchannels. The choice of the type and the frequency of the backchannels to be displayed is performed considering the agent's personality traits. The personality of the agent is defined in terms of two dimensions, extroversion and neuroticism. We link agents with a higher level of extroversion to a higher tendency to perform more backchannels than introverted ones, and we link neuroticism to less mimicry production and more response and reactive signals sent. We run a perception study to test these relations in agent-user interactions, as evaluated by third parties. We find that the selection of the frequency of backchannels performed by our algorithm contributes to the correct interpretation of the agent's behaviour in terms of personality traits.