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. 2020 Jul;37(7):657-669.
doi: 10.1002/da.23020. Epub 2020 May 7.

Automated voice biomarkers for depression symptoms using an online cross-sectional data collection initiative

Larry Zhang  1 , Radhika Duvvuri  2 , Kiranmayi K L Chandra  3 , Theresa Nguyen  4 , Reza H Ghomi  1   5
Affiliations

Automated voice biomarkers for depression symptoms using an online cross-sectional data collection initiative

Larry Zhang et al. Depress Anxiety. 2020 Jul.

Abstract

Importance: Depression is an illness affecting a large percentage of the world's population throughout the lifetime. To date, there is no available biomarker for depression detection and tracking of symptoms relies on patient self-report.

Objective: To explore and validate features extracted from recorded voice samples of depressed subjects as digital biomarkers for suicidality, psychomotor disturbance, and depression severity.

Design: We conducted a cross-sectional study over the course of 12 months using a frequently visited web form version of the PHQ9 hosted by Mental Health America (MHA) to ask subjects for anonymous voice samples via a separate web form hosted by NeuroLex Laboratories. Subjects were asked to provide demographics, answers to the PHQ9, and two voice samples.

Setting: Online only.

Participants: Users of the MHA website.

Main outcomes and measures: Performance of statistical models using extracted voice features to predict psychomotor disturbance, suicidality, and depression severity as indicated by the PHQ9.

Results: Voice features extracted from recorded audio of depressed subjects were able to predict PHQ9 question 9 and total scores with an area under the curve of 0.821 and a mean absolute error of 4.7, respectively. Psychomotor Disturbance prediction was less powerful with an area under the curve of 0.61.

Conclusion and relevance: Automated voice analysis using short recordings of patient speech may be used to augment depression screen and symptom management.

Keywords: biological markers; depression; mood disorders; suicide; web-based.

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References

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