In recent years, automatic and objective classification of mood disorders, such as depression using various signals (speech, face, brain wave) has got popularity.
Clinical depression is a severe mental health disorder and it has a wide range of potential symptoms including like strong negative conceptualization and observable psychomotor retardation.
Current diagnosis tool for depression depends almost exclusively on self-report and clinical opinion by psychiatrist. But it has critical weakness that monitoring patient’s status is very difficult. Most of people suffer from depression, which also has no strong will to revisit hospital for medical care. So developing automatic monitoring/classification system for patients with depression can have a major impact on society.
Speech has strong cues for classifying patient’s status. Speech in patients with depression has reflection that often described as being stagnant with diminished prosody. Paralinguistic analysis of depressed speech has shown that physiological symptoms relating to depression affects vocal tract properties.
The aim of this project is to investigate the utility of affective sensing methods for automated depression analysis, which can assist psychiatrist in depression diagnosis and monitoring. Remote depression diagnosis/monitoring system can cover a large number of depressive patients.
Subin Lee, Ziwon Hyung, Jongkyu Shin, Joengsoo Park
Seoul National University, Seoul National University Bundang Hospital