时间频率特征融合网络用于基于语音的自动抑郁症识别和评估
IEEE journal of biomedical and health informatics
|May 29, 2025
概括
这项研究介绍了TTFNet,这是一种新的深度学习方法,用于使用混合语音功能自动检测抑郁症. TTFNet通过分析时间和频率域语音特征来增强早期查.
科学领域:
- 计算语言学计算语言学
- 精神病学是一个精神病学.
- 机器学习 机器学习
背景情况:
- 抑郁症的诊断缺乏客观,方便的早期查方法.
- 抑郁症患者和健康个体之间存在声学差异.
研究的目的:
- 提出一种使用混合语音功能自动检测抑郁症的方法.
- 通过先进的深度学习技术来增强早期抑郁症查.
主要方法:
- 开发了TTFNet,这是一个使用混合语音功能的深度学习模型.
- 用于频域特征的四子表示 (Log-Mel光谱图).
- 集成的四视觉LSTM和sLSTM,用于时间特征的 wav2vec2.0.
- 利用XConformer块和双路径融合来实现功能互补性.
主要成果:
- 在多个数据集 (AVEC 2013,AVEC 2014,DAIC-WOZ,E-DAIC) 上,TTFNet的性能优于当前最先进的方法.
- 在抑郁症识别和严重程度预测任务中取得了卓越的表现.
结论:
- 提议的TTFNet方法为客观和方便的抑郁症查提供了一个有希望的方法.
- 混合语音功能和先进的深度学习架构显著提高了抑郁症检测准确度.
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