Realistic Visual Speech Synthesis Based on Hybrid Concatenation Method

Abstract

This paper presents a realistic visual speech synthesis based on the hybrid concatenation method. Unlike previous methods based on phoneme level unit selection or hidden Markov model (HMM), etc., the hybrid concatenation method uses a frame level-based unit selection method combined with a fused HMM, and is able to generate more expressive and stable facial animations. The fused HMM can be used to explicitly model the loose synchronization of tightly coupled streams, with much better results than a normal HMM for audiovisual mapping. After fused HMM is created, facial animation is generated via the unit selection method at the frame level by using the fused HMM output probabilities. To accelerate the computing efficiency of the unit selection on a large corpus, this paper also proposes a two-layer Viterbi search method in which only the subsets that have been selected in the first layer are further checked in the second layer. Using this idea, the system has been successfully integrated into real-time applications. Furthermore, the paper also proposes a mapping method to generate emotional facial expressions from neutral facial expressions based on Gaussian mixture models (GMMs). Final experiments prove that the method described can output synthesized facial parameters with high quality. Compared with other audiovisual mapping methods, our method has better performance with respect to expressiveness, stability, and system running speed.