HiQuE: Hierarchical Question Embedding Network for Multimodal Depression Detection
This work addresses early intervention for individuals with depression by improving detection accuracy in clinical settings, though it is incremental as it builds on existing multimodal approaches.
The paper tackles automated depression detection from clinical interview videos by modeling the hierarchical structure of interview questions, and their HiQuE framework outperforms existing multimodal models on the DAIC-WOZ dataset.
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.