Method Matters: Enhancing Voice-Based Depression Detection With a New Data Collection Framework

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 n = 34, std 2.8%, p-value 0.01). We further show that combining voice-only acoustic features with a single self-report question (subject unit of distress [SUDs]) significantly improves predictive accuracy. While relying on SUDs is not always good practice, our data collection setting lacked incentives to misrepresent depression status; SUDs were highly reliable, giving 86% accuracy; adding acoustic features raises it to 92%, exceeding the stand-alone potential of SUDs with a p-value 0.1. Further data collection will enhance accuracy, supporting a rapid, noninvasive depression detection method that overcomes clinical barriers. These findings offer a promising tool for early depression detection across clinical settings.