用对抗训练减少语音抑郁检测中的性别偏差。
Domain Adversarial Training for Mitigating Gender Bias in Speech-based Mental Health Detection
- 将性别视为不同领域，通过对抗训练优化语音模型
- 在E-DAIC数据集上F1-score提升13.29个百分点
- 适合关注公平性与临床实用性的AI心理健康研究者
基于语音的AI模型在检测抑郁症和创伤后应激障碍（PTSD）方面展现出强大潜力，提供了一种无创且低成本的心理健康评估方式。然而，这些模型常存在性别偏差，导致预测不公且不准。本研究提出一种领域对抗训练方法，显式考虑语音检测中性别差异。具体地，将不同性别视为不同领域，并将其信息融入预训练语音基础模型。在E-DAIC数据集上的验证表明，该方法显著提升检测性能，相较于基线模型F1-score最高提升13.29个百分点。结果凸显了在人工智能驱动的心理健康评估中解决人口统计差异的重要性。
原文摘要 · Abstract (English)
Speech-based AI models are emerging as powerful tools for detecting depression and the presence of Post-traumatic stress disorder (PTSD), offering a non-invasive and cost-effective way to assess mental health. However, these models often struggle with gender bias, which can lead to unfair and inaccurate predictions. In this study, our study addresses this issue by introducing a domain adversarial training approach that explicitly considers gender differences in speech-based depression and PTSD detection. Specifically, we treat different genders as distinct domains and integrate this information into a pretrained speech foundation model. We then validate its effectiveness on the E-DAIC dataset to assess its impact on performance. Experimental results show that our method notably improves detection performance, increasing the F1-score by up to 13.29 percentage points compared to the baseline. This highlights the importance of addressing demographic disparities in AI-driven mental health assessment.
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