用大模型分析青少年语音，识别自杀风险，准确率达74%。
Leveraging Large Language Models for Spontaneous Speech-Based Suicide Risk Detection
- 用大语言模型提取语音语义特征，结合传统声学特征
- 在比赛中取得74%准确率，排名第一
- 适合心理健康筛查、临床辅助诊断场景
早期识别自杀风险对预防自残行为至关重要。因此，当前研究重点聚焦于探索与自杀风险相关的模式和标志物。本文报告了我们在首届SpeechWellness挑战赛（SW1）中的成果，旨在利用语音这一非侵入性且易获取的心理健康指标，识别存在自杀风险的青少年。我们的方法以大语言模型（LLM）为核心进行特征提取，同时融合传统声学与语义特征。该方法在测试集上达到74%的准确率，在SW1挑战赛中位列第一。结果表明，基于大语言模型的语音分析在自杀风险评估中具有显著潜力。
原文摘要 · Abstract (English)
Early identification of suicide risk is crucial for preventing suicidal behaviors. As a result, the identification and study of patterns and markers related to suicide risk have become a key focus of current research. In this paper, we present the results of our work in the 1st SpeechWellness Challenge (SW1), which aims to explore speech as a non-invasive and easily accessible mental health indicator for identifying adolescents at risk of suicide.Our approach leverages large language model (LLM) as the primary tool for feature extraction, alongside conventional acoustic and semantic features. The proposed method achieves an accuracy of 74\% on the test set, ranking first in the SW1 challenge. These findings demonstrate the potential of LLM-based methods for analyzing speech in the context of suicide risk assessment.
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