Automated voice biomarkers for depression symptoms using an online cross-sectional data collection initiative
Open accessAbstract
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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Several digital biomarkers have been investigated for MDD characterization and diagnosis, including physical activity patterns, voice features, light exposure measurements, mobile phone GPS, and normal smartphone usage duration and frequency.
“In the recent years, several digital biomarkers have been investigated for MDD characterization and diagnosis such as measure patterns of physical activity (24, 25) features from voice samples (26, 27), light exposure measurements (25), mobile phone global positioning systems (GPS) and normal usage of smartphones such as usage duration and frequency (22).”
From A narrative review of digital biomarkers in the management of major depressive disorder and treatment-resistant forms (2023) · cites this paper as [27]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 25 · Cited by 67
Research works printed but not linked: 1
- Gratch, J., Arstein, R., Lucas, G., Stratou, G., Scherer, S., Nazarian, A., … Morency, L.-P. (2014). The distress analysis interview corpus of human and computer, Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14) (pp. 3123-3128). Reykjavik, Iceland: European Language Resources Association (ELRA).
- Authors
- Zhang, L., Duvvuri, R., Chandra, K. K. L., Nguyen, T., & Ghomi, R. H.
- Affiliations
- University of Washington (US)
- Johns Hopkins University (US)
- Mental Health America (US)
- Corresponding author
- Ghomi, Reza H.
- Venue
- Depression and Anxiety
- Publisher
- Wiley
- Published
- 2020-05-07
- Volume
- 37
- Issue
- 7
- Pages
- 657-669
- ISSN
- 1091-4269 (ISSN-L)
- 1520-6394
- OpenAlex
- W3022135431
- Cited by
- 67
- References
- 25
- Citations by year
- 2026: 5 · 2025: 16 · 2024: 16 · 2023: 10 · 2022: 13 · 2021: 7
- FWCI
- 7.26
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Depression and Anxiety (publishedVersion)
- SSRN Electronic Journal (acceptedVersion)
- PubMed (publishedVersion)
- Funders
- NeuroLex Laboratories
- Mental Health America
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Keywords
- depression, vocal biomarkers, digital biomarkers, voice analysis, suicidality, psychomotor disorders, depression severity, PHQ-9, depression screening, automated speech analysis, online data collection
- MeSH terms
- Cross-Sectional Studies, Depression, Humans, Mental Health, Speech, Biomarkers
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
