Inferring Depression and Affect from Application Dependent Meta Knowledge.

This paper outlines our contribution to the 2014 edition of the AVEC competition. It comprises classification results and considerations for both the continuous affect recognition sub-challenge and also the depression recognition sub-challenge. Rather than relying on statistical features that are normally extracted from the raw audio-visual data we propose an approach based on abstract meta information about individual subjects and also prototypical task and label dependent templates to infer the respective emotional states. The results of the approach that were submitted to both parts of the challenge significantly outperformed the baseline approaches. Further, we elaborate on several issues about the labeling of affective corpora and the choice of appropriate performance measures.