使用语音特征在抑郁和非抑郁群体中神经病症的预测建模
Qian Luo1, Yazheng Di1, Tingshao Zhu1
1Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China.
Journal of affective disorders
|February 11, 2024
概括
先进的语音功能可以改善神经病的预测. 在女性 (尤其是抑郁症患者) 中,将声学和扬声器嵌入模型 (i-vector,x-vector) 结合起来,可提高精度超过60%.
科学领域:
- 心理学和计算语言学
- 利用先进的机器学习进行心理特征评估.
背景情况:
- 神经病症显著影响精神和身体健康,并影响自杀风险.
- 以前基于语音的神经病的评估依赖于基本的声学特征,缺乏标准化.
- 先进的扬声器嵌入模型为更细致的语音分析提供了潜力.
研究的目的:
- 开发和验证高级语音特征提取方法来评估神经病症.
- 为了比较传统的声学特征与i-vector和x-vector嵌入.
- 在抑郁和非抑郁的女性子组中调查神经病症预测.
主要方法:
- 收集了来自3,580名抑郁症和4,016名健康妇女的临床采访话语数据.
- 从语音数据中提取低级声学描述器,i向量和x向量特征.
- 雇员支持向量回归 (SVR) 预测神经病症得分,比较个人和组合特征集.
主要成果:
- 结合声学和扬声器嵌入功能的SVR模型实现了R平方≥0.3和0.56.6的相关性.
- 在健康和抑郁的人群中,神经病症的预测分类准确度超过了60%.
- 功能缩小到300个维度优化了模型性能.
结论:
- 整合多样化的语音特征,包括先进的嵌入,显著提高神经病预测的准确性.
- 语音分析显示了评估神经病症的前景,特别是在特定的临床和人口群体内.
- 这项研究为进一步研究基于语音的心理评估提供了基础.
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