通过与完全自动化的虚拟人类进行随意对话,通过语音和文本检测抑郁症
Lucía Gómez-Zaragozá1, Alberto Altozano1, Jose Llanes-Jurado1
1HUMAN-Tech Institute, Universitat Politècnica de València, 46022, Valencia, Spain.
Artificial intelligence in medicine
|November 16, 2025
概括
这项研究使用虚拟人来分析随意对话中的语音和文本,发现各种互动中的语音线索是检测抑郁症的关键指标. 这种方法提供了一种更客观的方法来识别抑郁症状.
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.
- 人与计算机的互动.
背景情况:
- 抑郁症是一个日益增长的全球健康问题,具有诊断挑战.
- 目前的方法往往低估了由于主观性的抑郁症.
- 需要新的方法来客观和早期检测抑郁症状.
研究的目的:
- 在开放式对话中使用虚拟人类 (VHs) 调查用于检测抑郁症状的基于语音的标记.
- 开发和评估基于语音和文本的抑郁症检测计算模型.
- 为了比较多式联络抑郁症检测的对话级和转向级聚合策略.
主要方法:
- 收集了101名与VH互动的参与者 (42名有抑郁症状) 的语音和文本数据 (DEPTALK数据集).
- 利用预训练的变压器模型来生成语音和文本嵌入.
- 使用极端梯度增强和门式循环单位,分别在对话和转级别进行分类.
- 评估的单模 (语音,文本) 和多模 (融合) 模型.
主要成果:
- 与多式联络融合的对话级聚合获得了最高的F1评分 (0.648).
- 基于语音的分析,特别是散文,显示了比单独的文本更强烈的抑郁症线索.
- 转换级聚合改进了基于文本的检测 (F1=0.505),但多式联络性能没有超过最佳对话级模型.
- 情感上多样化的相互作用聚合在一起,增强了抑郁症检测.
结论:
- 与VH进行的随意对话分析可以揭示显著的抑郁症指标,主要是通过声乐.
- 结合语音和文本的多模式分析可以提高抑郁症检测的准确性.
- 虚拟人类为客观和可扩展的抑郁症状查提供了一个有希望的途径.
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