基于情感和基于主题的语言任务引起的抑郁症的声学特征
Qunxing Lin1, Xiaohua Wu1, Yueshiyuan Lei1
1Digital Mental Health and Risk Identification and Control Lab, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, Guangdong, China.
BMJ mental health
|September 30, 2025
概括
这项研究表明,使用情感多样化的图像任务进行语音分析可以有效地检测主要抑郁症 (MDD). 积极价值图像任务是最准确的,提供了一个可扩展的,非侵入性的查工具.
科学领域:
- 计算精神病学是一种计算精神病学.
- 语音分析 语音分析
- 机器学习 机器学习
背景情况:
- 大型抑郁症 (MDD) 在全球范围内往往被诊断不足.
- 目前的诊断方法在很大程度上依赖于主观的自我报告和临床采访.
- 需要客观,可扩展的MDD诊断工具.
研究的目的:
- 调查基于语音的分类模型在区分MDD个体与健康对照中的有效性.
- 评估在MDD检测中用于语音分析的情感和主题多样化的图像描述任务的使用.
- 为了比较长期短期记忆 (LSTM) 神经网络与传统机器学习模型的性能.
主要方法:
- 120名参与者 (59名患有MDD,61名对照) 完成了涉及情感价值图像 (积极,中性,负面) 和主题感知测试 (TAT) 的语言任务.
- 从语音反应中提取声学特征,并使用LSTM网络进行分类.
- 用SHapley添加式扩展 (SHAP) 来解释特征,传统模型作为比较器.
主要成果:
- 在LSTM模型的表现优于传统的分类器,有效地捕捉动态的语音模式.
- 涉及正价值图像的语音任务在MDD检测中产生了最高的准确性 (87.5%).
- SHAP分析确定了与音调相关的和光谱特征作为关键的歧视因素,语音持续时间在任务之间有很大的差异.
结论:
- 情感丰富和投射性语音任务显著增强自动MDD检测.
- 积极价值刺激显示出识别MDD的最大区分能力.
- 基于语音的模型为早期抑郁症查提供了一个可扩展,非侵入性和潜在的跨文化适用的方法.
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