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. 2015 Aug:4:75-79.
doi: 10.1016/j.copsyc.2014.12.010.

Automated Audiovisual Depression Analysis

Jeffrey M Girard  1 , Jeffrey F Cohn  1
Affiliations

Automated Audiovisual Depression Analysis

Jeffrey M Girard et al. Curr Opin Psychol. 2015 Aug.

Abstract

Analysis of observable behavior in depression primarily relies on subjective measures. New computational approaches make possible automated audiovisual measurement of behaviors that humans struggle to quantify (e.g., movement velocity and voice inflection). These tools have the potential to improve screening and diagnosis, identify new behavioral indicators of depression, measure response to clinical intervention, and test clinical theories about underlying mechanisms. Highlights include a study that measured the temporal coordination of vocal tract and facial movements, a study that predicted which adolescents would go on to develop depression based on their voice qualities, and a study that tested the behavioral predictions of clinical theories using automated measures of facial actions and head motion.

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Figures

Figure 1
Figure 1
Standard structure of analysis for automated audiovisual behavior analysis
See this image and copyright information in PMC

References

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    1. Girard JM, Cohn JF, Sayette MA, Jeni LA, De la Torre F. Spontaneous facial expression in unscripted social interactions can be measured automatically. Behavior Research Methods. In Press. - PMC - PubMed
    1. Yang Y, Fairbairn CE, Cohn JF. Detecting depression severity from vocal prosody. IEEE Transactions on Affective Computing. 2013;4:142–150. [The authors demonstrate that naive listeners can perceive depression severity from vocal recordings of patients and clinical interviewers. Then, using automated analyses and a longitudinal sample, they explore the relationship between symptom severity and both intrapersonal and interpersonal prosodic measures. This study demonstrates that depression can affect the behavior of interacting parties (i.e., the interviewers).] - PMC - PubMed
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