16th Annual Conference of the International Speech Communication Association

Dresden, Germany
September 6-10, 2015

Relevance Vector Machine for Depression Prediction

Nicholas Cummins (1), Vidhyasaharan Sethu (1), Julien Epps (1), Jarek Krajewski (2)

(1) University of New South Wales, Australia
(2) Universität Wuppertal, Germany

The objective and automated monitoring of depression using behavioral signals is confounded by the wide clinical profile of this commonly occurring mood disorder. This paper introduces Relevance Vector Machines, a novel method for predicting clinical depression scores from paralinguistic cues. It highlights many of the advantages RVM can offer depression prediction; sparsity, implicit noise characterization, an explicit probabilistic output and heterogeneous mapping property which allow one or more arbitrary, non-linear, transform to be used in conjunction with a RVM. Results indicate that RVMs can perform as strongly as Support Vector Regression in a brute-forcing paradigm. Of particular interest is the heterogeneous mapping property which improves RVM performance without requiring an expensive, in terms of data and time, search of the operating parameter space.

Full Paper

Bibliographic reference.  Cummins, Nicholas / Sethu, Vidhyasaharan / Epps, Julien / Krajewski, Jarek (2015): "Relevance vector machine for depression prediction", In INTERSPEECH-2015, 110-114.