ArticleDepression and anxiety2025
Method Matters: Enhancing Voice-Based Depression Detection With a New Data Collection Framework.
Article in Depression and anxiety, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Voice and speech as digital biomarkers of mental health: a scoping review of clinical interpretability, data collection and translational readiness.Frontiers in digital health · 2026Pooled it
- A causal discovery framework for digital phenotyping.Scientific reports · 2026Article
- Screening for depression risk via smartphone narratives with fully fine-tuned WavLM.Scientific reports · 2026Article
- Stable Longitudinal Screening of Latent Physiological Dysregulation from Psychometric Data Using Machine Learning.Bioengineering (Basel, Switzerland) · 2026Article
- Low-burden speech-based screening of depressive symptoms using ultra-short speech samples.Frontiers in psychiatry · 2026Article
- A multimodal feature fusion model integrating voice acoustic features and early key risk factors for early screening of postpartum depression: a prospective short-term longitudinal observational study.Frontiers in public health · 2026Observational
- Multimodal AI-based systems in major depressive disorder: a review of clinical and translational applications.Frontiers in digital health · 2026Review
- HMDF-Net: a transfer learning-based heterogeneous multimodal dynamic fusion network for depression detection among inmates in correctional facilities.Frontiers in psychiatry · 2026Article
- Understanding emotional responses to music using machine learning: evidence from a public dataset.Frontiers in psychology · 2026Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Depression accounts for a major share of global disability-adjusted life-years (DALYs). Diagnosis typically requires a psychiatrist or lengthy self-assessments, which can be challenging for symptomatic individuals. Developing reliable, noninvasive, and accessible detection methods is a healthcare priority. Voice analysis offers a promising approach for early depression detection, potentially improving treatment access and reducing costs. This paper presents a novel pipeline for depression detection that addresses several critical challenges in the field, including data imbalance, label quality, and model generalizability. Our study utilizes a high-quality, high-depression-prevalence dataset collected from a specialized chronic pain clinic, enabling robust depression detection even with a limited sample size. We obtained a lift in the accuracy of up to 15% over the 50-50 baseline in our 52-patient dataset using a 3-fold cross-validation test (which means the train set is
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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.