Weighted pairwise Gaussian likelihood regression for depression score prediction

Abstract

This paper presents a technique in which feature vectors are mapped onto ordinal ranges of clinical depression scores using weighted pairwise Gaussians. The position of a test vector with respect to these partitions is used to perform depression score prediction. Results found on a set of spectral and formant based speech characteristics indicate the potential of this technique for performing depression score prediction. Key results on the AVEC 2013 development set indicate that the inclusion of weights and Bayesian adaptation improves system performance by 16.5% - 18.5% when compared to using an unweighted non-adapted system. Fusing results from Bayesian adapted models corresponding to different feature spaces offers up to 8% further improvement. Further, fusion consistently improves performance on both the AVEC 2013 development and test set, in contrast to conventional regressor fusion.