  With more than 300 million people depressed worldwide, depression is a global
problem. Due to access barriers such as social stigma, cost, and treatment
availability, 60% of mentally-ill adults do not receive any mental health
services. Effective and efficient diagnosis relies on detecting clinical
symptoms of depression. Automatic detection of depressive symptoms would
potentially improve diagnostic accuracy and availability, leading to faster
intervention. In this work, we present a machine learning method for measuring
the severity of depressive symptoms. Our multi-modal method uses 3D facial
expressions and spoken language, commonly available from modern cell phones. It
demonstrates an average error of 3.67 points (15.3% relative) on the
clinically-validated Patient Health Questionnaire (PHQ) scale. For detecting
major depressive disorder, our model demonstrates 83.3% sensitivity and 82.6%
specificity. Overall, this paper shows how speech recognition, computer vision,
and natural language processing can be combined to assist mental health
patients and practitioners. This technology could be deployed to cell phones
worldwide and facilitate low-cost universal access to mental health care.
