Interview-Based Depression Detection Using LLM-Based Text Restatement and Emotion Lexicon

Depression is a mental health disorder that significantly impacts modern society. Developing accurate depression detection models by leveraging discriminant features from multimedia or physiological data can aid medical professionals in making informed diagnoses. According to psychological studies, emotion is a critical indicator of depression. However, emotion has not been utilized as a central role in current research on assistive depression detection, usually serving as a supplementary information source or a guidance for integrating diverse data modalities. In contrast to existing studies, we investigate the feasibility of detecting depression by concentrating on emotion information. Specifically, focusing on modeling emotion feature representation during interviews, we propose an interview-based depression detection model via leveraging large language (LLM) based text restatement and emotion lexicon (IDD-LTE). In this model, we employ LLM to enhance text quality through restatement to address the potentially low quality of interview text data. Using an emotion lexicon, the open contents in restated texts are mapped to a fixed-size matrix representation that captures the interviewee's emotional state and mood swings during the conversation, serving as the fundamental representation for the following discriminant feature learning. The proposed IDD-LTE model is evaluated on four primary datasets for depression detection. The promising results confirm the feasibility and effectiveness of our model.