Emotional Audio-Visual Speech Synthesis Based on PAD

Audio-visual speech synthesis is the core function for realizing face-to-face human-computer communication. While considerable efforts have been made to enable talking with computer like people, how to integrate the emotional expressions into the audio-visual speech synthesis remains largely a problem. In this paper, we adopt the notion of Pleasure-Displeasure, Arousal-Nonarousal, and Dominance-Submissiveness (PAD) 3-D-emotional space, in which emotions can be described and quantified from three different dimensions. Based on this new definition, we propose a unified model for emotional speech conversion using Boosting-Gaussian mixture model (GMM), as well as a facial expression synthesis model. We further present an emotional audio-visual speech synthesis approach. Specifically, we take the text and the target PAD values as input, and employ the text-to-speech (TTS) engine to first generate the neutral speeches. Then the Boosting-GMM is used to convert the neutral speeches to emotional speeches, and the facial expression is synthesized simultaneously. Finally, the acoustic features of the emotional speech are used to modulate the facial expression in the audio-visual speech. We designed three objective and five subjective experiments to evaluate the performance of each model and the overall approach. Our experimental results on audio-visual emotional speech datasets show that the proposed approach can effectively and efficiently synthesize natural and expressive emotional audio-visual speeches. Analysis on the results also unveil that the mutually reinforcing relationship indeed exists between audio and video information.