Automatic depression detection technology based on speech and text data is one of the current research hotspots. However, numerous current models may inadequately incorporate the individual characteristics important for improving detection precision. This leads to misjudgment when the model deals with a depressed patient whose behavior significantly differs from the majority of patients. Thus, the goal of this research is to extract individual characteristics from speech and text data among individuals and develop an efficient and stable personalized model for depression detection. We propose a personalized multimodal depression detection model (PMDDM). The proposed model generates personalized embeddings for each individual by utilizing both speech and text features, while learning depressive representations and integrating them with personalized embeddings for joint representations. Lastly, the model receives the joint representations to predict the individual's depression status. We evaluated the proposed model using two datasets related to depression detection, MODMA and MIDD. Compared to the best baseline methods, our method improved the recognition accuracy by 5.88% and 3.84% on the MODMA and MIDD datasets, respectively. The F1 scores increased by 4.55% and 3.45%, respectively. In the cross-dataset generalizability experiments, our method still outperformed the best baseline methods in F1 score and accuracy. Results suggest that the effective integration of personalized information can significantly improve the accuracy and generalizability of depression detection models. Our research provides novel insights into personalized depression detection.

Facial movement-based depression recognition has garnered considerable attention in automatic depression detection field in recent years. Current methods typically seek to establish a uniform depression discrimination criterion and often overlook inter-individual differences in facial movements, which lead to a decrease in the accuracy of models. A closer examination of the clinical diagnostic criteria for depression reveals that individuals with depression exhibit a wide variety of symptoms. This suggests that even individuals with similar levels of depression may exhibit significantly different facial movements. To address this challenge, this study introduces a novel framework for depression assessment—Multiscale Video Adaptive Graph Network (MVAGN). By leveraging the information transmission capabilities of Graph Neural Network (GNN) to balance the relationship between individual differences and commonalities to achieve more accurate depression identification. MVAGN consists of three modules: the Multiscale Video Feature Extractor (MVFE), the Hybrid Feature Interaction Graph Module (HFIGM), and the Adaptive Hybrid Graph Network (AHGN). MVFE extracts multi-scale features from the video, HFIGM transforms these features into graph representations, and AHGN facilitates information transfer between nodes to reduce individual feature differences, ultimately providing a more precise estimation of depression severity. Experiments conducted on the AVEC2013 and AVEC2014 datasets demonstrate that MVAGN achieves state-of-the-art performance. This paper proposes that building a fuzzy system with a compact kernel and a loose periphery provides a viable path for recognizing depression.