抑郁症检测方法基于语音和文本的多式融合
Zhenrong Xu1, Yuan Gao1, Fang Wang1
1School of Biomedical Engineering, South-Central Minzu University, Wuhan, 430074, China.
Scientific reports
|July 2, 2025
概括
这项研究引入了一种新的融合模型,用于使用语音和文本数据自动检测抑郁症. 该模型显著提高了确定抑郁症和估计其严重程度的准确性,而不是单一模式的方法.
科学领域:
- 计算精神病学是一种计算精神病学.
- 医疗保健中的人工智能
- 机器学习用于心理健康
背景情况:
- 抑郁症是一种广泛的心理健康问题,需要早期发现才能有效治疗.
- 目前用于抑郁症的诊断方法可能是主观和不一致的.
- 自动检测系统为客观和高效的评估提供了潜力.
研究的目的:
- 开发和评估用于自动抑郁症检测和严重程度估计的双模融合模型.
- 整合语音和文本数据,以提高诊断准确度.
- 为了比较融合模型与单模式方法的性能.
主要方法:
- 使用了Wav2Vec 2.0和BERT,分别用于语音和文本功能提取.
- 采用多尺度卷积层和Bi-LSTM网络进行特征融合.
- 实现了适应性聚合,用于集成的特征处理和分类.
- 对CMDC和DAIC数据集的统一系统进行了评估,用于抑郁症分类和PHQ-8严重程度估计.
主要成果:
- 融合模型在CMDC (0.0103为语音,0.2017为文本) 和DAIC (0.0645为语音,0.2589为文本) 数据集上都显示出更好的F1分数.
- 严重性预测的根平均平方误差 (RMSE) 在CMDC上减少了0.5186,在DAIC上减少了1.9901.
- 该模型有效地捕获和整合了语音和文本模式的多层次信息.
结论:
- 拟议的双模融合模型显著提高了自动低压检测的准确性和可靠性.
- 这种方法为客观,准确地评估抑郁症及其严重程度提供了一个有希望的工具.
- 整合多模式数据是心理健康诊断计算方法的关键进步.
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