  We propose a novel robust and efficient Speech-to-Animation (S2A) approach
for synchronized facial animation generation in human-computer interaction.
Compared with conventional approaches, the proposed approach utilizes phonetic
posteriorgrams (PPGs) of spoken phonemes as input to ensure the cross-language
and cross-speaker ability, and introduces corresponding prosody features (i.e.
pitch and energy) to further enhance the expression of generated animation.
Mixture-of-experts (MOE)-based Transformer is employed to better model
contextual information while provide significant optimization on computation
efficiency. Experiments demonstrate the effectiveness of the proposed approach
on both objective and subjective evaluation with 17x inference speedup compared
with the state-of-the-art approach.
