Individualized Gesturing Outperforms Average Gesturing - Evaluating Gesture Production in Virtual Humans

Abstract. How does a virtual agent’s gesturing behavior influence the user’s perception of communication quality and the agent’s personality? This question was investigated in an evaluation study of co-verbal iconic gestures produced with the Bayesian network-based production model GNetIc. A network learned from a corpus of several speakers was compared with networks learned from individual speaker data, as well as two control conditions. Results showed that automatically GNetIc-generated gestures increased the perceived quality of an object description given by a virtual human. Moreover, the virtual agent showing gesturing behavior generated with individual speaker networks was rated more positively in terms of likeability, competence and human-likeness. Key words: Evaluation, Gesture Generation, Inter-subjective Differences

• Type: Journal
• Year: 2010
• Where: IVA
• Authors: Kirsten Bergmann, Stefan Kopp, Friederike Eyssel
• Keywords: Gesture, Individual Speaker, Intelligent Agents, IVA 2010, Virtual Agent