基于语音特征的小学和中学学生的抑郁症状查:中国江苏的一年纵向研究
Jiawen Tang1, Xin Wang1, Wendi Zhu1
1School of Public Health, Nanjing Medical University, Nanjing, China; School of Public Health, Nanjing Medical University, Nanjing, China & Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing City, Jiangsu Province, China.
Journal of affective disorders
|December 21, 2025
概括
语音分析可以识别学生的抑郁风险. 通过机器学习分析的语音特征,为学校人群的早期检测和个性化评估提供了一种新的方法.
科学领域:
- 机器学习在心理健康中的应用
- 语音信号处理 语音信号处理
- 儿童和青少年精神病学
背景情况:
- 在学龄人群中,抑郁症是一个重大问题.
- 早期发现抑郁风险对于及时干预至关重要.
- 声学语音特征为心理状态评估提供了一种非侵入性方法.
研究的目的:
- 开发和验证可解释的机器学习模型,用于学生抑郁风险预测.
- 识别暗示抑郁风险的关键声学语音特征.
- 评估模型在不同学生子组中的表现.
主要方法:
- 利用了江苏省学校评估推进儿童健康反应 (SEARCH) 计划的数据.
- 采用了四种机器学习方法,包括光梯度增强机 (LightGBM).
- 应用沙普利添加式解释 (SHAP) 来解释特征,并选择了十个声学预测器.
主要成果:
- 轻GBM模型实现了0.718的AUC,用于抑郁风险预测.
- 关键预测因素包括光谱特征 (蜂蜜光谱图,光谱对比度,MFCC) 和形特征.
- 最终的模型在小学学生中显示出稳定的性能和更高的疗效.
结论:
- 声学语音特征在识别学生抑郁风险方面是有效的.
- 这种方法为教育环境中个性化抑郁风险评估提供了一种新方法.
- 进一步的研究可以完善学生群体的基于语音的抑郁症检测工具.
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