AI Voice Analysis Detects Signs of Depression

Researchers published in Frontiers in Psychiatry evaluated machine learning analysis of voice recordings to detect current depressive episodes. Participants provided brief speech samples, which were processed using algorithms trained to identify acoustic markers of mood disturbance.

The system accurately distinguished individuals with depression from healthy controls, performing better than standard screening thresholds. Importantly, the method required only short recordings, making it practical for integration into routine check-ups or mobile applications.

This study highlights the potential of AI-powered voice biomarkers as a scalable, non-invasive tool for mental health screening. By enabling earlier detection of depressive episodes, voice analysis could help clinicians intervene sooner and guide patients toward effective support.