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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.22225 |
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| _version_ | 1866914114872279040 |
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| author | Luo, Yu Huang, Nan Yu, Sophie Xu, Hendry Wang, Jerry Wang, Colin Liu, Zhichao Zeng, Chen |
| author_facet | Luo, Yu Huang, Nan Yu, Sophie Xu, Hendry Wang, Jerry Wang, Colin Liu, Zhichao Zeng, Chen |
| contents | Depression, as a typical mental disorder, has become a prevalent issue significantly impacting public health. However, the prevention and treatment of depression still face multiple challenges, including complex diagnostic procedures, ambiguous criteria, and low consultation rates, which severely hinder timely assessment and intervention. To address these issues, this study adopts voice as a physiological signal and leverages its frequency-time dual domain multimodal characteristics along with deep learning models to develop an intelligent assessment and diagnostic algorithm for depression. Experimental results demonstrate that the proposed method achieves excellent performance in the classification task for depression diagnosis, offering new insights and approaches for the assessment, screening, and diagnosis of depression. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_22225 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Audio Frequency-Time Dual Domain Evaluation on Depression Diagnosis Luo, Yu Huang, Nan Yu, Sophie Xu, Hendry Wang, Jerry Wang, Colin Liu, Zhichao Zeng, Chen Computer Vision and Pattern Recognition Depression, as a typical mental disorder, has become a prevalent issue significantly impacting public health. However, the prevention and treatment of depression still face multiple challenges, including complex diagnostic procedures, ambiguous criteria, and low consultation rates, which severely hinder timely assessment and intervention. To address these issues, this study adopts voice as a physiological signal and leverages its frequency-time dual domain multimodal characteristics along with deep learning models to develop an intelligent assessment and diagnostic algorithm for depression. Experimental results demonstrate that the proposed method achieves excellent performance in the classification task for depression diagnosis, offering new insights and approaches for the assessment, screening, and diagnosis of depression. |
| title | Audio Frequency-Time Dual Domain Evaluation on Depression Diagnosis |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.22225 |