Acoustic-based machine learning approaches for depression detection in Chinese university students
The integration of acoustic features and LDA model demonstrates a high accuracy in distinguishing depression among Chinese university students, suggesting its potential utility in rapid and large-scale depression screening. MFCC may serve as objective and valid features for the automated identification of depression on Chinese university campuses.
Authors
- Y. Z. Zhou (ORCID: https://orcid.org/0009-0002-1167-9197)
- Gu‐Wei Ji (ORCID: https://orcid.org/0000-0003-1736-062X)
- Wei Zheng (ORCID: https://orcid.org/0000-0002-8228-2675)
- Ruiling Zhang (ORCID: https://orcid.org/0000-0003-3636-2268)
- Rongxun Liu (ORCID: https://orcid.org/0009-0005-3147-3593)
- Yuanle Chen (ORCID: https://orcid.org/0009-0003-3899-6421)
- Fei Wang (ORCID: https://orcid.org/0000-0002-7650-6345)
- Shisen Qin (ORCID: https://orcid.org/0009-0003-8663-5620)
- Xingliang Xiong (ORCID: https://orcid.org/0000-0002-8265-7203)
- Fengyi Liu (ORCID: https://orcid.org/0009-0007-0597-5498)
- Yong Hong Meng (ORCID: https://orcid.org/0009-0005-3173-3519)
- Yange Wei (ORCID: https://orcid.org/0000-0002-0303-5239)
Institutions
- Peking University (CN)
- Henan Psychiatric Hospital (CN)
- Nanjing Brain Hospital (CN)
- Peking University Sixth Hospital (CN)
- Henan Medical University (CN)
- Guangzhou Medical University (CN)
Publication Details
- Journal
- Frontiers in Public Health
- Published
- 2025-05-15
- DOI
- https://doi.org/10.3389/fpubh.2025.1561332
- Citations
- 5
- Primary Topic
- Emotion and Mood Recognition
- Type
- article
- Field-Weighted Citation Impact
- 10.59
Funders
- National Natural Science Foundation of China
- Henan University