在低资源环境中,使用wav2vec 2.0的转移学习改进语音抑郁的检测.
Xu Zhang1, Xiangcheng Zhang2, Weisi Chen1
1School of Software Engineering, Xiamen University of Technology, Xiamen, 361024, China.
Scientific reports
|April 25, 2024
概括
这项研究引入了一种新的转移学习方法,用于使用语音分析检测抑郁症,克服有限的数据挑战. 该模型实现了高精度,为低资源环境中心理健康检测提供了有希望的解决方案.
科学领域:
- 计算语言学计算语言学
- 医疗保健中的人工智能
- 心理健康信息学心理健康信息学
背景情况:
- 抑郁症是一种广泛的心理健康障碍,具有重大的全球影响.
- 有效的抑郁症检测模型因缺乏注释语音数据而受到阻碍.
- 现有的深度学习方法需要大量的标记数据集.
研究的目的:
- 利用转移学习开发一种有效的基于语音的抑郁症检测模型.
- 为了应对抑郁症检测研究中有限的注释数据的挑战.
- 为了提高模型识别与抑郁相关的语言模式的能力.
主要方法:
- 通过微调 wav2vec 2.0 来进行特征提取,利用转移学习.
- 集成的1D-CNN和注意力聚合,用于高级分段级特征表示.
- 采用LSTM和自我注意力机制来权衡与抑郁症相关的语音段.
主要成果:
- 在DAIC-WOZ数据集上获得了79%的F1得分.
- 在CMDC数据集上获得了90.53%的F1得分.
- 在基于语音的抑郁症检测中表现优于现有的基线模型.
结论:
- 拟议的转移学习模型在通过语言检测抑郁症方面表现出高效.
- 这种方法在资源有限,注释数据有限的环境中特别有效.
- 这项研究为可扩展的心理健康查和诊断提供了一个可行的工具.
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