Exploring Machine Learning and Language Models for Multimodal Depression Detection

Javier Si Zhao Hong; Timothy Zoe Delaya; Sherwyn Chan Yin Kit; Pai Chet Ng; Xiaoxiao Miao

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arXiv · 2508.20805

Exploring Machine Learning and Language Models for Multimodal Depression Detection

Javier Si Zhao Hong·Timothy Zoe Delaya·Sherwyn Chan Yin Kit·Pai Chet Ng·Xiaoxiao Miao

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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.

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Javier Si Zhao Hong, Timothy Zoe Delaya, Sherwyn Chan Yin Kit, Pai Chet Ng, Xiaoxiao Miao. 2025-08-28. Exploring Machine Learning and Language Models for Multimodal Depression Detection. https://arxiv.org/abs/2508.20805

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