The early detection of suicide risk is important since it enables the
intervention to prevent potential suicide attempts. This paper studies the
automatic detection of suicide risk based on spontaneous speech from
adolescents, and collects a Mandarin dataset with 15 hours of suicide speech
from more than a thousand adolescents aged from ten to eighteen for our
experiments. To leverage the diverse acoustic and linguistic features embedded
in spontaneous speech, both the Whisper speech model and textual large language
models (LLMs) are used for suicide risk detection. Both all-parameter
finetuning and parameter-efficient finetuning approaches are used to adapt the
pre-trained models for suicide risk detection, and multiple audio-text fusion
approaches are evaluated to combine the representations of Whisper and the LLM.
The proposed system achieves a detection accuracy of 0.807 and an F1-score of
0.846 on the test set with 119 subjects, indicating promising potential for
real suicide risk detection applications.

对于自杀风险的早期检测非常重要，本研究通过青少年自发言语对自杀风险进行自动检测，并收集了汉语数据集，包括来自千名年龄在十到十八岁之间的青少年 15 小时的自杀言语进行实验。通过使用 Whisper 语音模型和大型文本语言模型 (LLM) 来检测自杀风险，采用了全参数微调和参数效率微调方法来适应预训练模型，评估了多种音频文本融合方法来结合 Whisper 和 LLM 的表示，所提出的系统在包括 119 名受试者的测试集上达到了 0.807 的检测准确度和 0.846 的 F1 分数，表明具有潜在的真实自杀风险检测应用前景。