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 have collected a novel dataset of speech recordings from $20$
patients from which 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.

为了解决在紧急医学中对存在自杀风险的患者延迟专科精神评估和护理的问题，我们提出了一种非侵入性的基于语音的自动自杀风险评估方法，结合录音数据和元数据集成，有效提高了自动自杀风险评估的准确性。