Abstract

We propose a data driven approach for modeling head motion behavior in human dyadic interactions, by establishing a structure for unconstrained natural head movement. Using recordings of couples' conversations in real psychotherapy sessions, we first track the head of each subject, compute the head motion and detect active versus non-active intervals. For detected active intervals, we use a sliding window to collect motion sequences. Linear Prediction Coefficients are used to represent the sequence, based on which we train a Gaussian Mixture Model (GMM) such that each mixture would ideally associate with one type of prototypical movement, which we will refer to as a “kineme”. For each complete interaction session, we compute the sum of posterior probabilities of all sequences over the GMM normalized by session length to predict specific “low” versus “high” expert annotated behavior code scores for …

Date

2013

Authors

Bo Xiao, Panayiotis G Georgiou, Brian Baucom, Shrikanth S Narayanan

Conference

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

Pages

3766-3770

Publisher

IEEE