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Objective methods for reliable detection of concealed depression

Solomon, Cynthia; Valstar, Michel F.; Morriss, Richard K.; Crowe, John


Cynthia Solomon

Professor of Psychiatry & Community Mental Health

John Crowe


Recent research has shown that it is possible to automatically detect clinical depression from audio-visual recordings. Before considering integration in a clinical pathway, a key question that must be asked is whether such systems can be easily fooled. This work explores the potential of acoustic features to detect clinical depression in adults both when acting normally and when asked to conceal their depression. Nine adults diagnosed with mild to moderate depression as per the Beck Depression Inventory (BDI-II) and Patient Health Questionnaire (PHQ, Chang, 2012) were asked a series of questions and to read a excerpt from a novel aloud under two different experimental conditions. In one, participants were asked to act naturally and in the other, to suppress anything that they felt would be indicative of their depression. Acoustic features were then extracted from this data and analyzed using paired t-tests to determine any statistically significant differences between healthy and depressed participants. Most features that were found to be significantly different during normal behavior remained so during concealed behavior. In leave-one-subject-out automatic classification studies of the 9 depressed subjects and 8 matched healthy controls, an 88% classification accuracy and 89% sensitivity was achieved. Results remained relatively robust during concealed behavior, with classifiers trained on only non-concealed data achieving 81% detection accuracy and 75% sensitivity when tested on concealed data. These results indicate there is good potential to build deception-proof automatic depression monitoring systems.


Solomon, C., Valstar, M. F., Morriss, R. K., & Crowe, J. (2015). Objective methods for reliable detection of concealed depression. Frontiers in ICT, 2,

Journal Article Type Article
Acceptance Date Mar 25, 2015
Online Publication Date Apr 15, 2015
Publication Date Apr 15, 2015
Deposit Date Jul 30, 2015
Publicly Available Date Jul 30, 2015
Journal Frontiers in ICT
Electronic ISSN 2297-198X
Publisher Frontiers Media
Peer Reviewed Peer Reviewed
Volume 2
Article Number 5
Public URL
Publisher URL
Copyright Statement Copyright information regarding this work can be found at the following address:


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