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Cited by 277

The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.

Detecting Depression Severity from Vocal Prosody

Open access
Yang, Ying, Fairbairn, Catherine, Cohn, Jeffrey F.·2013·IEEE Transactions on Affective Computing
DOI 10.1109/t-affc.2012.38
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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 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.

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Metadata

Authors
Yang, Y., Fairbairn, C., & Cohn, J. F.
Venue
IEEE Transactions on Affective Computing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2013-04
Volume
4
Issue
2
Pages
142-150
ISSN
1949-3045 (ISSN-L)
DOI
10.1109/t-affc.2012.38
OpenAlex
W2088846517
Cited by
277
References
51
FWCI
19.25
Type
journal article
Language
en
Open access
green

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