{"ID":2877193,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.20805","arxiv_id":"2508.20805","title":"Exploring Machine Learning and Language Models for Multimodal Depression Detection","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2508.20805","url_pdf":"https://arxiv.org/pdf/2508.20805v1","authors":"[\"Javier Si Zhao Hong\",\"Timothy Zoe Delaya\",\"Sherwyn Chan Yin Kit\",\"Pai Chet Ng\",\"Xiaoxiao Miao\"]","published":"2025-08-28T14:07:07Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\",\"cs.SD\"]","methods":"[\"Transformer\",\"Large Language Model\",\"Language Model\"]","has_code":false}