  Extensive efforts have been devoted to recognizing facial action units (AUs).
However, it is still challenging to recognize AUs from spontaneous facial
displays especially when they are accompanied with speech. Different from all
prior work that utilized visual observations for facial AU recognition, this
paper presents a novel approach that recognizes speech-related AUs exclusively
from audio signals based on the fact that facial activities are highly
correlated with voice during speech. Specifically, dynamic and physiological
relationships between AUs and phonemes are modeled through a continuous time
Bayesian network (CTBN); then AU recognition is performed by probabilistic
inference via the CTBN model.
  A pilot audiovisual AU-coded database has been constructed to evaluate the
proposed audio-based AU recognition framework. The database consists of a
"clean" subset with frontal and neutral faces and a challenging subset
collected with large head movements and occlusions. Experimental results on
this database show that the proposed CTBN model achieves promising recognition
performance for 7 speech-related AUs and outperforms the state-of-the-art
visual-based methods especially for those AUs that are activated at low
intensities or "hardly visible" in the visual channel. Furthermore, the CTBN
model yields more impressive recognition performance on the challenging subset,
where the visual-based approaches suffer significantly.
