Non-Invasive Suicide Risk Prediction Through Speech Analysis
<|reference_start|>Non-Invasive Suicide Risk Prediction Through Speech Analysis: 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.<|reference_end|>
@article{amiriparian2024non-invasive,
    title={Non-Invasive Suicide Risk Prediction Through Speech Analysis},
    author={Shahin Amiriparian, Maurice Gerczuk, Justina Lutz, Wolfgang Strube,
  Irina Papazova, Alkomiet Hasan, Alexander Kathan, Bj"orn W. Schuller},
    journal={arXiv preprint arXiv:2404.12132},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.12132},
    primaryClass={cs.SD cs.CL eess.AS}
}