Behavior Computing
States and Traits
Spoken Dialogs
Other: Signal Modeling for Understanding
Head motion synchrony and its correlation to affectivity in dyadic interactions
Behavioral synchrony, or entrainment, is a phenomenon of great interest to psychologists and a challenging construct to quantify. In this work we study the synchrony behavior of head motion in human dyadic interactions. We model head motion using Gaussian Mixture Model (GMM) of line spectral frequencies extracted from the motion vectors of the head. We quantify interlocutor head motion similarity through the Kullback-Leibler divergence of the GMM posteriors of their respective motion sequences. We use an audiovisual database of distressed couple interactions, extensively annotated by psychologists, to test two hypotheses using the derived similarity measure. We validate the first hypothesis — that people are more likely to increase their degree of synchrony as the interaction progresses — by comparing the first and second halves of the interaction. The second hypothesis tests if the relative change of the similarity measure from these two halves is significantly correlated with the behavioral annotation by the domain experts. This work underscores the importance of head motion as an interaction cue, and the feasibility of using it in a computational model for synchrony behavior.
Illustration of the processing steps in Sec.3
Illustration of the processing steps in Sec.3
Head motion | Synchrony | Behavioral signal processing | Entrainment | Linear prediction | Gaussian mixture model
Chi-Chun Lee
Publication Date
IEEE International Conference on Multimedia and Expo (ICME)
2013 IEEE International Conference on Multimedia and Expo (ICME)
IEEE Xplore