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.

翻译：急诊科中具有自杀倾向风险的患者难以及时获得专业的精神评估与护理，这一缺口阻碍了在危急情况下提供充分的心理健康支持。为解决此问题，我们提出了一种基于语音的非侵入式自动自杀风险评估方法。在本研究中，我们收集了来自 $20$ 名患者的新型语音记录数据集。我们提取了三组特征，包括 wav2vec、可解释的语音与声学特征，以及基于深度学习的频谱表示。随后，我们采用留一受试者交叉验证的方式进行二元分类以评估自杀风险。我们最有效的语音模型实现了 $66.2\,\%$ 的平衡准确率。此外，我们发现将语音模型与一系列患者元数据（如自杀尝试史或枪支接触史）相结合可提升整体结果。元数据整合后的平衡准确率达到 $94.4\,\%$，绝对提升了 $28.2\,\%$，这证明了我们所提方法在急诊医学中自动评估自杀风险的有效性。