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Automated voice biomarkers for depression symptoms using an online cross-sectional data collection initiative
Larry Zhang1, Radhika Duvvuri2, Kiranmayi K L Chandra3
1Neurology, University of Washington, Seattle, Washington.
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.
