The utilization of automated depression detection significantly enhances early intervention for individuals experiencing depression. Despite numerous proposals on automated depression detection using recorded clinical interview videos, limited attention has been paid to considering the hierarchical structure of the interview questions. In clinical interviews for diagnosing depression, clinicians use a structured questionnaire that includes routine baseline questions and follow-up questions to assess the interviewee's condition. This paper introduces HiQuE (Hierarchical Question Embedding network), a novel depression detection framework that leverages the hierarchical relationship between primary and follow-up questions in clinical interviews. HiQuE can effectively capture the importance of each question in diagnosing depression by learning mutual information across multiple modalities. We conduct extensive experiments on the widely-used clinical interview data, DAIC-WOZ, where our model outperforms other state-of-the-art multimodal depression detection models and emotion recognition models, showcasing its clinical utility in depression detection.

翻译：自动化抑郁检测技术的应用显著增强了对抑郁个体的早期干预能力。尽管已有许多利用临床访谈视频进行自动化抑郁检测的研究，但针对访谈问题层次结构的考量仍较为有限。在用于诊断抑郁的临床访谈中，临床医生采用结构化问卷，其中包含常规基线问题及用于评估受访者状况的后续追问问题。本文提出HiQuE（层次化问题嵌入网络），这是一种新型抑郁检测框架，能够利用临床访谈中主要问题与后续追问问题之间的层次关系。通过跨多模态学习互信息，HiQuE可有效捕捉每个问题在抑郁诊断中的重要性。我们在广泛使用的临床访谈数据集DAIC-WOZ上进行了大量实验，结果表明该模型性能优于其他最先进的多模态抑郁检测模型与情绪识别模型，展现了其在抑郁检测中的临床应用价值。