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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.12088 |
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| _version_ | 1866916595355353088 |
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| author | Qin, Jinghui Liu, Changsong Tang, Tianchi Liu, Dahuang Wang, Minghao Huang, Qianying Zhang, Rumin |
| author_facet | Qin, Jinghui Liu, Changsong Tang, Tianchi Liu, Dahuang Wang, Minghao Huang, Qianying Zhang, Rumin |
| contents | Mental disorders, such as anxiety and depression, have become a global concern that affects people of all ages. Early detection and treatment are crucial to mitigate the negative effects these disorders can have on daily life. Although AI-based detection methods show promise, progress is hindered by the lack of publicly available large-scale datasets. To address this, we introduce the Multi-Modal Psychological assessment corpus (MMPsy), a large-scale dataset containing audio recordings and transcripts from Mandarin-speaking adolescents undergoing automated anxiety/depression assessment interviews. MMPsy also includes self-reported anxiety/depression evaluations using standardized psychological questionnaires. Leveraging this dataset, we propose Mental-Perceiver, a deep learning model for estimating mental disorders from audio and textual data. Extensive experiments on MMPsy and the DAIC-WOZ dataset demonstrate the effectiveness of Mental-Perceiver in anxiety and depression detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_12088 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mental-Perceiver: Audio-Textual Multi-Modal Learning for Estimating Mental Disorders Qin, Jinghui Liu, Changsong Tang, Tianchi Liu, Dahuang Wang, Minghao Huang, Qianying Zhang, Rumin Computers and Society Mental disorders, such as anxiety and depression, have become a global concern that affects people of all ages. Early detection and treatment are crucial to mitigate the negative effects these disorders can have on daily life. Although AI-based detection methods show promise, progress is hindered by the lack of publicly available large-scale datasets. To address this, we introduce the Multi-Modal Psychological assessment corpus (MMPsy), a large-scale dataset containing audio recordings and transcripts from Mandarin-speaking adolescents undergoing automated anxiety/depression assessment interviews. MMPsy also includes self-reported anxiety/depression evaluations using standardized psychological questionnaires. Leveraging this dataset, we propose Mental-Perceiver, a deep learning model for estimating mental disorders from audio and textual data. Extensive experiments on MMPsy and the DAIC-WOZ dataset demonstrate the effectiveness of Mental-Perceiver in anxiety and depression detection. |
| title | Mental-Perceiver: Audio-Textual Multi-Modal Learning for Estimating Mental Disorders |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2408.12088 |