机器学习方法用于识别使用心理健康帮助电话辅导员的声音的同情心:算法开发和验证
Ruvini Sanjeewa1, Ravi Iyer1, Pragalathan Apputhurai1
1School of Health Sciences, Swinburne University of Technology, Hawthorn, PO Box 218, John Street, Melbourne, 3122, Australia, 61 422587030.
JMIR formative research
|April 16, 2025
概括
这项研究确定了14个与同情辅导员演讲相关的声乐特征. 一种二进制后勤回归模型在检测声学线索的同情心中显示出最高的准确性,尽管仍然存在挑战.
科学领域:
- 心理学 心理学 心理学
- 语音分析 语言分析
- 计算语言学 计算语言学
背景情况:
- 心理健康热线服务依赖于同情的沟通.
- 在辅导员的演讲中检测同情是有效支持的关键.
- 语音线索提供了一个潜在的,但尚未被充分探索的,共感评估的途径.
研究的目的:
- 为了识别在同情辅导员演讲中存在的特定声音特征.
- 开发一种算法,通过声乐特征自动检测同情心.
- 为辅导员和交谈代理人创建一个培训工具,以增强语音同情.
主要方法:
- 分析了57个心理健康辅助电话,其中有两名心理学背景评分员.
- 声格特征的提取和统计选择方法的应用 (LASSO,前向选择).
- 开发和比较通用添加混合模型 (GAMM),二进制逻辑回归和随机森林模型用于同情心分类.
主要成果:
- 确定了14个与同情性言语相关的显著声乐特征.
- 二元逻辑回归模型实现了最高的预测准确性 (AUC=0.617),超过了GAMM和随机森林模型.
- 统计测试证实了二元逻辑回归模型在同理心分类方面的优越性.
结论:
- 仅仅从声格特征中检测同理心是具有挑战性的.
- 建议在未来的研究中采用结合面部表情和语言的多模式方法.
- 需要更大,更多样化的数据集和多个评分器来提高声音同理心的概括性和理解.
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