Accurate Visual Speech Synthesis Based on Diviseme Unit Selection and Concatenation

This paper presents a novel speech driven accurate realistic visual speech synthesis approach. Firstly, an audio visual instance database is built for different viseme context combinations, i.e. diviseme units, using 100 audio visual speech sentences of a female speaker. Then a diviseme instance selection algorithm is introduced to choose the optimal diviseme instances for the viseme contexts in the input speech, considering both the concatenation smoothness of the image sequences, and matching of the mouth movements to the acoustic pronunciation process, as well the intensity of the input speech. Finally mouth image sequences of corresponding viseme segments in the selected diviseme instances are time warped and blended to construct the mouth images of the final animation. Visual speech synthesis experiments and subjective evaluation results show that mouth animations can he obtained which are not only realistic with clear and smooth mouth images, but also in good accordance with the acoustic pronunciation and intensity of the input speech.