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.
@article{arxiv.2408.12088,
title = {Mental-Perceiver: Audio-Textual Multi-Modal Learning for Estimating Mental Disorders},
author = {Jinghui Qin and Changsong Liu and Tianchi Tang and Dahuang Liu and Minghao Wang and Qianying Huang and Rumin Zhang},
journal= {arXiv preprint arXiv:2408.12088},
year = {2025}
}