从现实生活中的演讲中检测日常压力,使用声学和语义信息
Peixian Lu1,2, Liuxing Tsao1,3, Liang Ma1
1Lab of Enhanced Human‑Machine Collaborative Decision‑Making, National Key Laboratory of Human Factors Engineering, Department of Industrial Engineering,Tsinghua University, Beijing, P.R. China.
Ergonomics
|November 25, 2024
概括
从语音中使用机器学习模型检测日常压力是可行的. 声学和语义信号的融合显著提高了压力检测的准确性,接近了人类在工作场所健康方面的能力.
科学领域:
- 语音分析 语音分析
- 机器学习是机器学习.
- 职业健康 职业健康 职业健康 职业健康
背景情况:
- 每天检测压力对工作场所的安全和健康至关重要.
- 自然语言是一种有前途的方法来检测心理压力.
- 现有的方法可能无法完全捕捉现实生活压力的细微差别.
研究的目的:
- 开发和评估机器学习模型,以使用现实生活中的语言检测日常压力.
- 研究声学和语义语音信号对应力检测准确性的贡献.
- 将机器学习模型性能与人类压力识别能力进行比较.
主要方法:
- 从一个在线聊天平台的压力催化室收集了现实生活中的语音数据.
- 开发了利用声学和语义语音特征的机器学习分类器.
- 使用精度和F1得分评估模型性能,与人类性能进行比较.
主要成果:
- 只有声学模型的准确度达到74.25%,F1得分达到83.73%.
- 声学和语义信号的融合提高了性能,达到81.20%的准确性和87.46%的F1分数.
- 机器学习的性能接近于人类的压力识别能力.
结论:
- 使用机器学习从真实语言中检测日常压力是可行的和有效的.
- 语义信息显著提高了压力检测模型的准确性.
- 开发的模型为现实生活中的压力监测和干预策略提供了潜力.
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