HMM-based Synthesis of Emotional Facial Expressions during Speech in Synthetic Talking Heads.

One of the research goals in the human-computer interaction community is to build believable Embodied Conversational Agents, that is, agents able to communicate complex information with human-like expressiveness and naturalness. Since emotions play a crucial role in human communication and most of them are expressed through the face, having more believable ECAs implies to give them the ability of displaying emotional facial expressions. This paper presents a system based on Hidden Markov Models (HMMs) for the synthesis of emotional facial expressions during speech. The HMMs were trained on a set of emotion examples in which a professional actor uttered Italian non-sense words, acting various emotional facial expressions with different intensities. The evaluation of the experimental results, performed comparing the “synthetic examples” (generated by the system) with a reference “natural example” (one of the actor’s examples) in three different ways, shows that HMMs for emotional facial expressions synthesis have some limitations but are suitable to make a synthetic Talking Head more expressive and realistic.