Mental-Perceiver: Audio-Textual Multi-Modal Learning for Estimating Mental Disorders

• URL: http://arxiv.org/abs/2408.12088v2
• Date: Tue, 04 Feb 2025 02:32:07 GMT
• Title: Mental-Perceiver: Audio-Textual Multi-Modal Learning for Estimating Mental Disorders
• Authors: Jinghui Qin, Changsong Liu, Tianchi Tang, Dahuang Liu, Minghao Wang, Qianying Huang, Rumin Zhang,
• Abstract summary: Mental disorders, such as anxiety and depression, have become a global concern that affects people of all ages. Although AI-based detection methods show promise, progress is hindered by the lack of publicly available large-scale datasets. We propose Mental-Perceiver, a deep learning model for estimating mental disorders from audio and textual data.
• Score: 9.222458475626652
• License:
• Abstract: 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.

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