Staircase Regression in OA RVM, Data Selection and Gender Dependency in AVEC 2016

Abstract

Within the field of affective computing, human emotion and disorder/disease recognition have progressively attracted more interest in multimodal analysis. This submission to the Depression Classification and Continuous Emotion Prediction challenges for AVEC2016 investigates both, with a focus on audio subsystems. For depression classification, we investigate token word selection, vocal tract coordination parameters computed from spectral centroid features, and gender-dependent classification systems. Token word selection performed very well on the development set. For emotion prediction, we investigate emotionally salient data selection based on emotion change, an output-associative regression approach based on the probabilistic outputs of relevance vector machine classifiers operating on low-high class pairs (OA RVM-SR), and gender-dependent systems. Experimental results from both the development and test sets show that the RVM-SR method under the OA framework can improve on OA RVM, which performed very well in the AV+EC2015 challenge.