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Detecting Depression Severity from Vocal Prosody
April-June 2013 (vol. 4 no. 2)
pp. 142-150
Ying Yang, University of Pittsburgh, Pittsburgh
Catherine Fairbairn, University of Pittsburgh, Pittsburgh
Jeffrey F. Cohn, University of Pittsburgh, Pittsburgh and Carnegie Mellon University, Pittsburgh
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.
Index Terms:
Interviews,Switches,Timing,Speech,Atmospheric measurements,Particle measurements,Audio recording,hierarchical linear modeling (HLM),Prosody,switching pause,vocal fundamental frequency,depression,interpersonal influence
Citation:
Ying Yang, Catherine Fairbairn, Jeffrey F. Cohn, "Detecting Depression Severity from Vocal Prosody," IEEE Transactions on Affective Computing, vol. 4, no. 2, pp. 142-150, April-June 2013, doi:10.1109/T-AFFC.2012.38
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