压力语音:用于实时个性化压力检测和适应性心理支持的语音驱动框架
Laraib Umer1,2,3, Javaid Iqbal1, Yasar Ayaz1,2
1School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan.
Diagnostics (Basel, Switzerland)
|November 27, 2025
概括
本研究引入了使用大型语言模型 (LLM) 和语音分析的实时压力检测系统. 该框架提供适应性,多式联络反,推进数字心理健康解决方案.
科学领域:
- 计算语言学计算语言学
- 数字心理健康数字心理健康
- 医疗保健中的人工智能
背景情况:
- 传统的压力监测缺乏实时响应能力,并依赖主观报告.
- 现有的语音驱动式压力检测仅限于文本分析,单语言设置或仅检测输出.
- 大型语言模型 (LLM) 提供了适应性,语音驱动的压力检测的机会.
研究的目的:
- 开发和评估一个实时,语音驱动的压力检测框架,使用LLMs.
- 评估各种LLM变体的压力检测性能.
- 验证框架的可行性,包容性和以用户为中心的适用性.
主要方法:
- 开发了一个整合音频录制,语音转换和基于LLM的语言分析的框架.
- 在5个基准数据集上评估了9个LLM变体,使用零射击和少射击学习.
- 通过准确性,精度,回忆,F1分数和延迟来评估性能;进行了以用户为中心的验证.
主要成果:
- 微调的精确调整显著改善了LLM的性能,LLaMA和RoBERTa显示了最好的结果.
- 延迟分析表明模型大小,准确性和实时响应能力之间的权衡.
- 多语言支持和多模式输出增强了包容性,用户对可用性和采用潜力的强烈反.
结论:
- 实时,LLM驱动的压力检测在技术上是强大的,实际上是可行的.
- 该框架超越了仅检测模型,向可扩展的,包容性的数字心理健康解决方案迈进.
- 多模式反和以用户为中心的验证是部署准备的应用程序的关键.
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