Semi-Structural Interview-Based Chinese Multimodal Depression Corpus Towards Automatic Preliminary Screening of Depressive Disorders

• Published in: IEEE transactions on affective computing Vol. 14; no. 4; pp. 2823 - 2838
• Main Authors: Zou, Bochao, Han, Jiali, Wang, Yingxue, Liu, Rui, Zhao, Shenghui, Feng, Lei, Lyu, Xiangwen, Ma, Huimin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acoustics; Affective computing; Annotations; Behavioral sciences; Depression; depressive disorder; Diagnosis; Feature extraction; Hospitals; Interviews; Medical screening; Mental depression; multimodal corpus; semi-structural interview; Visualization
• ISSN: 1949-3045; 1949-3045
• DOI: 10.1109/TAFFC.2022.3181210

Depression is a common psychiatric disorder worldwide. However, in China, a considerable number of patients with depression are not diagnosed, and most of them are not aware of their depression. Despite increasing efforts, the goal of automatic depression screening from behavioral indicators has not been achieved. A major limitation is the lack of available multimodal depression corpus in Chinese since linguistic knowledge is crucial in clinical practice. Therefore, we first carried out a comprehensive survey with psychiatrists from a renowned psychiatric hospital to identify key interview topics which are highly related to the diagnosis of depression. Then, a semi-structural interview study was conducted over a year with subjects who have undergone clinical diagnosis and professional assessment. After that, Visual, acoustic, and textual features were extracted and analyzed between the two groups, statistically significant differences were observed in all three modalities. Benchmark evaluations of both single modal and multimodal fusion methods of depression assessment were also performed. A multimodal transformer-based fusion approach achieved the best performance. Finally, the proposed Chinese Multimodal Depression Corpus (CMDC) was made publicly available after de-identification and annotation. Hopefully, the release of this corpus would promote the research progress and practical applications of automatic depression screening.