Affective state recognition in married couples' interactions using PCA-based vocal entrainment measures with multiple instance learning

Abstract

Recently there has been an increase in efforts in Behavioral Signal Processing (BSP), that aims to bring quantitative analysis using signal processing techniques in the domain of observational coding. Currently observational coding in fields such as psychology is based on subjective expert coding of abstract human interaction dynamics. In this work, we use a Multiple Instance Learning (MIL) framework, a saliency-based prediction model, with a signal-driven vocal entrainment measure as the feature to predict the affective state of a spouse in problem solving interactions. We generate 18 MIL classifiers to capture the variable-length saliency of vocal entrainment, and a cross-validation scheme with maximum accuracy and mutual information as the metric to select the best performing classifier for each testing couple. This method obtains a recognition accuracy of 53.93%, a 2.14% (4.13% relative) improvement over baseline model using Support Vector Machine. Furthermore, this MIL-based framework has potential for identifying meaningful regions of interest for further detailed analysis of married couples interactions. © 2011 Springer-Verlag.