AI-detected auditory findings of depression and suicide risk

Abstract

Objective: Suicide is a major global health concern and one of the leading causes of preventable death. Currently, there is a lack of objective data to assess suicide risk in individuals with depression. This study explores the potential of voice analysis as an objective tool for suicide risk assessment and improved diagnostic accuracy. Methods: Ninety participants divided into three groups (Near-Term Suicidal, Depressed, and Control) provided audio recordings of standardized text readings. Mel-frequency cepstral coefficients, deep learning features (VGGish), formants, and prosodic features were extracted and analyzed using a machine learning model. Results: Among the analyzed voice features, Mel-frequency cepstral coefficients were more successful for the high suicide risk or not'' and depression or high suicide risktasks, with an accuracy of 90.0% and 68.3%, respectively. For the depression or not'' task, VGGish representation achieved an accuracy of 73.3%. Conclusions: To our knowledge, this is the first study to employ VGGish features in the suicidality assessment. The findings demonstrate significant differences in vocal parameters across varying suicide risk levels, supporting the potential of voice analysis as a biomarker for suicide risk in depression. Given its non-invasive nature and real-time integration capability, voice analysis offers a promising approach for clinical applications.