Predicting face movements from speech acoustics using spectral dynamics

The paper introduces a new dynamical model which enhances the relationship between face movements and speech acoustics. Based on the autocorrelation of the acoustics and of the face movements, a causal and a non-causal filter are proposed to approximate dynamic features in the speech signals. The database consists of sentences recorded acoustically, and a Qualisys system is used to capture face movements, with 20 reflectors put on the face, simultaneously. Speech signals are represented by 16^th-order LSPs and log-energy. With the filtered dynamic features, the acoustic features account for more than 80% of the variance of face movements.