Evaluation of HMM-based visual laughter synthesis

In this paper we apply speaker-dependent training of Hidden Markov Models (HMMs) to audio and visual laughter synthesis separately. The two modalities are synthesized with a forced durations approach and are then combined together to render audio-visual laughter on a 3D avatar. This paper focuses on visual synthesis of laughter and its perceptive evaluation when combined with synthesized audio laughter. Previous work on audio and visual synthesis has been successfully applied to speech. The extrapolation to audio laughter synthesis has already been done. This paper shows that it is possible to extrapolate to visual laughter synthesis as well.