CCA based feature selection with application to continuous depression recognition from acoustic speech features

Abstract

In this study we make use of Canonical Correlation Analysis (CCA) based feature selection for continuous depression recognition from speech. Besides its common use in multi-modal/multi-view feature extraction, CCA can be easily employed as a feature selector. We introduce several novel ways of CCA based filter (ranking) methods, showing their relations to previous work. We test the suitability of proposed methods on the AVEC 2013 dataset under the ACM MM 2013 Challenge protocol. Using 17% of features, we obtained a relative improvement of 30% on the challenge's test-set baseline Root Mean Square Error.