Automatic audiovisual behavior descriptors for psychological disorder analysis.

We investigate the capabilities of automatic audiovisual nonverbal behavior descriptors to identify indicators of psychological disorders such as depression, anxiety, and post-traumatic stress disorder. Due to strong correlations between these disordersas measured with standard self-assessment questionnaires in this study, we focus our investigations in particular on a generic distress measure as identified using factor analysis. Within this work, we seek to confirm and enrich present state of the art, predominantly based on qualitative manual annotations, with automatic quantitative behavior descriptors. We propose a number of nonverbal behavior descriptors that can be automatically estimated from audiovisual signals. Such automatic behavior descriptors could be used to support healthcare providers with quantified and objective observations that could ultimately improve clinical assessment. We evaluate our work on the dataset called the Distress Assessment Interview Corpus (DAIC) which comprises dyadic interactions between a confederate interviewer and a paid participant. Our evaluation on this dataset shows correlation of our automatic behavior descriptors with the derived general distress measure. Our analysis also includes a deeper study of self-adaptor and fidgeting behaviors based on detailed annotations of where these behaviors occur.