Title :
Detecting Depression Severity from Vocal Prosody
Author :
Ying Yang ; Fairbairn, C. ; Cohn, J.F.
Author_Institution :
Human Rehabilitation & Neural Eng. Lab., Univ. of Pittsburgh, Pittsburgh, PA, USA
Abstract :
To investigate the relation between vocal prosody and change in depression severity over time, 57 participants from a clinical trial for treatment of depression were evaluated at seven-week intervals using a semistructured clinical interview for depression severity (Hamilton Rating Scale for Depression (HRSD)). All participants met criteria for major depressive disorder (MDD) at week one. Using both perceptual judgments by naive listeners and quantitative analyses of vocal timing and fundamental frequency, three hypotheses were tested: 1) Naive listeners can perceive the severity of depression from vocal recordings of depressed participants and interviewers. 2) Quantitative features of vocal prosody in depressed participants reveal change in symptom severity over the course of depression. 3) Interpersonal effects occur as well; such that vocal prosody in interviewers shows corresponding effects. These hypotheses were strongly supported. Together, participants´ and interviewers´ vocal prosody accounted for about 60 percent of variation in depression scores, and detected ordinal range of depression severity (low, mild, and moderate-to-severe) in 69 percent of cases (kappa = 0.53). These findings suggest that analysis of vocal prosody could be a powerful tool to assist in depression screening and monitoring over the course of depressive disorder and recovery.
Keywords :
audio recording; medical disorders; patient monitoring; patient treatment; psychology; speech processing; HRSD; Hamilton rating scale for depression; Interpersonal effects; MDD; Naive listeners; clinical trial; depression monitoring; depression scores; depression screening; depression severity detection; depression treatment; depressive disorder; depressive recovery; major depressive disorder; quantitative features; semistructured clinical interview; vocal prosody analysis; vocal recordings; vocal timing; Atmospheric measurements; Audio recording; Interviews; Particle measurements; Speech; Switches; Timing; Prosody; depression; hierarchical linear modeling (HLM); interpersonal influence; switching pause; vocal fundamental frequency;
Journal_Title :
Affective Computing, IEEE Transactions on
DOI :
10.1109/T-AFFC.2012.38