SonoMind:基于深度学习的语音分析,用于心理健康监测
1Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Srivilliputhur, Tamil Nadu.
Rivista di psichiatria
|February 10, 2026
概括
这项研究介绍了SonoMind,一种使用语音分析用于早期抑郁症检测的AI框架. 它实现了高精度,为心理健康专业人员提供可靠,非侵入性查工具.
科学领域:
- 人工智能的人工智能
- 临床心理学 临床心理学
- 生物医学工程 生物医学工程
背景情况:
- 抑郁症是一个重要的全球健康问题,由于耻辱和有限的查工具,经常被诊断不足.
- 现有的自动抑郁症检测方法通常依赖于问卷或多模式数据,在高效的,仅语音框架中存在差距.
- 需要用于抑郁症查的非侵入性,客观和保护隐私的诊断替代方案.
研究的目的:
- 提出SonoMind,一种适应性深度学习框架,用于使用语音信号早期检测抑郁症.
- 解决有效和可靠的仅语音临床检测框架的研究缺口.
- 为心理健康专业人员提供可扩展和支持的工具.
主要方法:
- 开发了一个名为SonoMind的自适应深度学习框架.
- 集成的可适应光谱配对用于降低噪声和同步声学用于同步的特征提取.
- 使用自适应的Krill-Wolf优化来选择特征,然后进行神经分类阶段.
- 评估了DAIC-WOZ临床访谈数据集的框架.
主要成果:
- 索诺智能实现了97.22%的准确性,100%的精度,95.92%的回忆力和97.92%的F1分数.
- 该系统显示平均绝对误差 (MAE) 为0.027和根平均平方误差 (RMSE) 为0.1666.
- 结果证实了该框架在基于语音的抑郁查中的稳健性和可靠性.
结论:
- SonoMind显示出作为心理健康专业人员可扩展和支持工具的巨大潜力.
- 该框架提供了一种可靠的非侵入性方法,用于使用语音信号早期检测抑郁症.
- 基于声音的查可以帮助克服与传统抑郁症评估方法相关的障碍.
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