ArticlePLOS mental health2026
ML-based detection of depressive profile through voice analysis in WhatsApp™ audio messages of Brazilian Portuguese Speakers.
Article in PLOS mental health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Voice-Based Screening of Depression and Anxiety Using Machine Learning and Deep Learning: A Scoping Review of Methods and Clinical Readiness.Life (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Depression is a prevalent mental health condition that significantly impacts individuals' daily lives, work productivity, relationships, and overall well-being. The lack of reliable biomarkers complicates screening, contributing to underdiagnosis. Depression's impact on voice and acoustic parameters enables differentiation between adaptive and non-adaptive mood profiles, offering potential classifiers for screening. This study evaluates the capability of seven distinct machine learning models to identify depression in speech samples. WhatsApp™ audio messages (WA), clinical, and sociodemographic data were collected from 160 individuals divided into two groups: one for algorithm development and the other for testing. Each group included patients with Major Depressive Disorder and healthy controls. In the test group, participants were interviewed using the Mini-International Neuropsychiatric Interview (MINI), and their WhatsApp™ audio recordings included both structured and semi-structured formats. After pre-processing the audio, 68 acoustic features were used to train the machine learning models. Results shows that: i) The algorithms evaluated WhatsApp™ audio recordings from the test group, achieving peak accuracies of 91.67% for women and 80% for men, with an AUC of 91.9% for women and 78.33% for men. ii) The accuracy of Machine Learning (ML) classification varies depending on the type of audio instruction provided. ML can classify, with reasonable accuracy, whether a WhatsApp™ audio message represents a depressive patient or a healthy individual. Future studies should further explore the relationship between voice characteristics, different mood profiles, and emotional states.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.