  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.
