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Non-Invasive Suicide Risk Prediction Through Speech Analysis

MCML Authors

Link to Profile Björn Schuller

Björn Schuller

Prof. Dr.

Principal Investigator

Health Informatics

Abstract

The delayed access to specialized psychiatric assessments and care for patients at risk of suicidal tendencies in emergency departments creates a notable gap in timely intervention, hindering the provision of adequate mental health support during critical situations. To address this, we present a non-invasive, speech-based approach for automatic suicide risk assessment. For our study, we collected a novel speech recording dataset from 20 patients. We extract three sets of features, including wav2vec, interpretable speech and acoustic features, and deep learning-based spectral representations. We proceed by conducting a binary classification to assess suicide risk in a leave-one-subject-out fashion. Our most effective speech model achieves a balanced accuracy of 66.2%. Moreover, we show that integrating our speech model with a series of patients' metadata, such as the history of suicide attempts or access to firearms, improves the overall result. The metadata integration yields a balanced accuracy of 94.4%, marking an absolute improvement of 28.2%, demonstrating the efficacy of our proposed approaches for automatic suicide risk assessment in emergency medicine.

inproceedings

EHB 2024

12th E-Health and Bioengineering Conference. IASI, Romania, Nov 14-15, 2024.

Authors

S. Amiriparian • M. Gerczuk • J. Lutz • W. Strube • I. Papazova • A. Hasan • A. Kathan • B. W. Schuller

Links

DOI

Research Area

 B3 | Multimodal Perception

BibTeXKey: AGL+24

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