In emergency medicine, timely intervention for patients at risk of suicide is often hindered by delayed access to specialised psychiatric care. To bridge this gap, we introduce a speech-based approach for automatic suicide risk assessment. Our study involves a novel dataset comprising speech recordings of 20 patients who read neutral texts. We extract four speech representations encompassing interpretable and deep features. Further, we explore the impact of gender-based modelling and phrase-level normalisation. By applying gender-exclusive modelling, features extracted from an emotion fine-tuned wav2vec2.0 model can be utilised to discriminate high- from low- suicide risk with a balanced accuracy of 81%. Finally, our analysis reveals a discrepancy in the relationship of speech characteristics and suicide risk between female and male subjects. For men in our dataset, suicide risk increases together with agitation while voice characteristics of female subjects point the other way.

在紧急医学领域，为了对潜在自杀风险患者进行及时干预，我们引入了一种基于语音的自动自杀风险评估方法。研究通过一个包括20名患者朗读中性文本的新型数据集，提取了四个涵盖可解释且深层特征的语音表示。通过性别差异建模和短语级归一化研究，我们发现利用情感微调的wav2vec2.0模型提取的特征能够以81%的平衡准确率区分高风险和低风险的自杀风险。最后，我们的分析揭示了女性和男性患者之间在语音特征和自杀风险关系上的差异：对于数据集中的男性，自杀风险和兴奋情绪是正相关的，而女性患者的声音特征则呈现相反的趋势。

探索自动自杀风险评估中的性别特定语言模式