论文标题
用于抑郁和焦虑检测的多模式深度学习系统
Multi-modal deep learning system for depression and anxiety detection
论文作者
论文摘要
焦虑和抑郁的传统筛查实践对有效监测和治疗这些疾病构成了障碍。但是，NLP和语音建模的最新进展允许文本，声学和手工制作的基于语言的功能共同构成未来心理健康筛查和状况检测的基础。言语是对个人认知状态的丰富且可用的洞察力来源，并且通过利用语音的不同方面，我们可以开发新的数字生物标志物来抑郁和焦虑。为此，我们提出了一个多模式系统，用于筛查自我管理的语音任务中的抑郁和焦虑。所提出的模型集成了音频和文本的深度学习功能，以及由临床验证的域知识告知的手工制作的功能。我们发现，具有深度学习特征的手工制作的功能可以提高我们的整体分类F1分数，而抑郁症的手工制作功能的基线仅为0.58至0.54，而焦虑症的抑郁症则从0.54到0.57。我们工作的发现表明，基于言语的抑郁症和焦虑生物标志物在数字健康的未来中具有巨大的希望。
Traditional screening practices for anxiety and depression pose an impediment to monitoring and treating these conditions effectively. However, recent advances in NLP and speech modelling allow textual, acoustic, and hand-crafted language-based features to jointly form the basis of future mental health screening and condition detection. Speech is a rich and readily available source of insight into an individual's cognitive state and by leveraging different aspects of speech, we can develop new digital biomarkers for depression and anxiety. To this end, we propose a multi-modal system for the screening of depression and anxiety from self-administered speech tasks. The proposed model integrates deep-learned features from audio and text, as well as hand-crafted features that are informed by clinically-validated domain knowledge. We find that augmenting hand-crafted features with deep-learned features improves our overall classification F1 score comparing to a baseline of hand-crafted features alone from 0.58 to 0.63 for depression and from 0.54 to 0.57 for anxiety. The findings of our work suggest that speech-based biomarkers for depression and anxiety hold significant promise in the future of digital health.
