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| Ying Yang, Catharine Fairbairn, Jeffrey F. Cohn, "Detecting Depression Severity from Vocal Prosody," IEEE Transactions on Affective Computing, vol. 99, no. 1, pp. 1, , 5555. | |||
| BibTex | x | ||
| @article{ 10.1109/T-AFFC.2012.38, author = {Ying Yang and Catharine Fairbairn and Jeffrey F. Cohn}, title = {Detecting Depression Severity from Vocal Prosody}, journal ={IEEE Transactions on Affective Computing}, volume = {99}, number = {1}, issn = {1949-3045}, year = {5555}, pages = {1}, doi = {http://doi.ieeecomputersociety.org/10.1109/T-AFFC.2012.38}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Affective Computing TI - Detecting Depression Severity from Vocal Prosody IS - 1 SN - 1949-3045 SP EP EPD - 1 A1 - Ying Yang, A1 - Catharine Fairbairn, A1 - Jeffrey F. Cohn, PY - 5555 KW - Social science methods or tools KW - Computer Applications KW - Social and Behavioral Sciences KW - Psychology KW - Affective Computing KW - Affect sensing and analysis KW - Modeling human emotion KW - Diagnosis or assessment KW - Affective computing applications KW - Health care VL - 99 JA - IEEE Transactions on Affective Computing ER - | |||
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 semi-structured clinical interview for depression severity (Hamilton Rating Scale for Depression: HRSD). All participants met criteria for Major Depressive Disorder at week 1. 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. And 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% of variation in depression scores, and detected ordinal range of depression severity (low, mild, and moderate-to-severe) in 69% 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:
Social science methods or tools,Computer Applications,Social and Behavioral Sciences,Psychology,Affective Computing,Affect sensing and analysis,Modeling human emotion,Diagnosis or assessment,Affective computing applications,Health care
Citation:
Ying Yang, Catharine Fairbairn, Jeffrey F. Cohn, "Detecting Depression Severity from Vocal Prosody," IEEE Transactions on Affective Computing, 29 Nov. 2012. IEEE computer Society Digital Library. IEEE Computer Society, <http://doi.ieeecomputersociety.org/10.1109/T-AFFC.2012.38>
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