Exploring Machine Learning and Language Models for Multimodal Depression Detection
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866914011102052352 |
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| author | Hong, Javier Si Zhao Delaya, Timothy Zoe Kit, Sherwyn Chan Yin Ng, Pai Chet Miao, Xiaoxiao |
| author_facet | Hong, Javier Si Zhao Delaya, Timothy Zoe Kit, Sherwyn Chan Yin Ng, Pai Chet Miao, Xiaoxiao |
| contents | This paper presents our approach to the first Multimodal Personality-Aware Depression Detection Challenge, focusing on multimodal depression detection using machine learning and deep learning models. We explore and compare the performance of XGBoost, transformer-based architectures, and large language models (LLMs) on audio, video, and text features. Our results highlight the strengths and limitations of each type of model in capturing depression-related signals across modalities, offering insights into effective multimodal representation strategies for mental health prediction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20805 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exploring Machine Learning and Language Models for Multimodal Depression Detection Hong, Javier Si Zhao Delaya, Timothy Zoe Kit, Sherwyn Chan Yin Ng, Pai Chet Miao, Xiaoxiao Computation and Language Artificial Intelligence Sound This paper presents our approach to the first Multimodal Personality-Aware Depression Detection Challenge, focusing on multimodal depression detection using machine learning and deep learning models. We explore and compare the performance of XGBoost, transformer-based architectures, and large language models (LLMs) on audio, video, and text features. Our results highlight the strengths and limitations of each type of model in capturing depression-related signals across modalities, offering insights into effective multimodal representation strategies for mental health prediction. |
| title | Exploring Machine Learning and Language Models for Multimodal Depression Detection |
| topic | Computation and Language Artificial Intelligence Sound |
| url | https://arxiv.org/abs/2508.20805 |