使用语音特征和机器学习模型来预测中国青少年的情绪和行为问题
Jinyu Li1, Yang Wang1,2, Fei Wang1,2,3
1The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.
Depression and anxiety
|June 24, 2025
概括
语音分析可以客观地预测青少年的情绪和行为问题,为主观报告提供了替代方案. 这项研究确定了早期干预的性别特异性语音标记.
科学领域:
- 青少年心理学 青少年心理学
- 机器学习应用程序 机器学习应用程序
- 语音信号处理 语音信号处理
背景情况:
- 目前青少年的情绪和行为问题评估依赖于主观,潜在的偏见报告.
- 需要客观标记来提高诊断准确度和减少偏差.
研究的目的:
- 研究语音信号作为青少年情绪和行为问题的客观预测因素.
- 探索机器学习技术,使用语音数据对过度活跃,情绪症状,行为问题和同行问题进行分类.
- 识别与这些问题相关的性别特异性语音特征.
主要方法:
- 分析了8215名青少年 (12-18岁) 的语音数据.
- 演讲特征的提取,包括音频频率 (MFCC),音频能量频谱 (MELS),音频特征 (PROS) 和音频特征 (FORM) 的演讲特征.
- 应用机器学习模型 (逻辑回归,支持向量机,梯度增强决策树) 来进行分类,通过AUC,F1得分和SHAP值来评估性能.
主要成果:
- 渐变增强决策树 (GBDT) 模型在预测多动性 (AUC=0.78) 和情绪症状 (AUC=0.74对于男性,0.66对于女性) 中显示出最高的准确性.
- 对于行为和同行问题,模型的表现不那么强大.
- 沙普利增量解释 (SHAP) 分析强调了某些语音特征的性别特异性重要性.
结论:
- 语音特征可以客观地预测青少年的情绪和行为问题.
- 识别出特定于性别的语音标志物为有针对性的干预提供了潜力.
- 这项研究支持开发基于语音的早期识别工具,以补充传统的主观方法.
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