基于语音的心理危机评估使用大语言模型
Speech-based Psychological Crisis Assessment using LLMs
AI总结：
本文提出利用大语言模型自动分类危机等级，通过引入语音学注入方法和增强推理训练策略，提升热线服务质量。
摘要：
心理支持热线为经历心理健康紧急情况的个人提供关键支持，但当前评估主要依赖人类操作员，其判断可能受专业经验影响且受限于人力资源。本文提出基于大语言模型（LLM）的自动化危机等级分类框架，是支持许多下游任务的关键指标，有助于提高热线服务质量。为更好地捕捉语音对话中的情感信号，我们引入了一种语音学注入方法，将识别出的非语音情感线索插入语音转录中，使基于LLM的推理能够纳入关键的声学细节。此外，我们提出了一种增强推理的训练策略，训练模型生成诊断推理链作为辅助任务，用作正则化器以提高分类性能。结合数据增强，我们的最终系统在五折交叉验证下的三类分类任务中实现了宏F1得分为0.802，准确率为0.805。
英文摘要：
Psychological support hotlines provide critical support for individuals experiencing mental health emergencies, yet current assessments largely rely on human operators whose judgments may vary with professional experience and are constrained by limited staffing resources. This paper proposes a large language model (LLM)-based framework for automated crisis level classification, a key indicator that supports many downstream tasks and improves the overall quality of hotline services. To better capture emotional signals in spoken conversations, we introduce a paralinguistic injection method that inserts identified non-verbal emotional cues into speech transcripts, enabling LLM-based reasoning to incorporate critical acoustic nuances. In addition, we propose a reasoning-enhanced training strategy that trains the model to generate diagnostic reasoning chains as an auxiliary task, which serves as a regulariser to improve classification performance. Combined with data augmentation, our final system achieves a macro F1-score of 0.802 and an accuracy of 0.805 on the three-class classification task under 5-fold cross-validation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
