Exploring Machine Learning and Language Models for Multimodal Depression Detection

2025-08-28

AI Summary

This study addresses automated multimodal depression detection by proposing a cross-modal modeling framework integrating audio, visual, and textual features. Methodologically, it introduces a synergistic architecture combining XGBoost for shallow temporal statistical feature learning, Transformer networks for intra-modal long-range dependency modeling, and large language models (LLMs) for enhanced textual semantic and contextual understanding, augmented by modality alignment and weighted fusion strategies. Comprehensive evaluation on public multimodal depression datasets demonstrates that the proposed fusion model significantly outperforms unimodal baselines—achieving AUC improvements of 4.2–7.8 percentage points—thereby validating the complementary strengths of heterogeneous models and the efficacy of multimodal representation learning. The work establishes a novel, interpretable, and robust paradigm for mental health status assessment, offering a reproducible technical pathway grounded in principled multimodal integration.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

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.

Problem

• Detecting depression using multimodal machine learning models
• Comparing XGBoost, transformers and LLMs across modalities
• Evaluating model performance on depression-related signal detection

Innovation

• XGBoost transformer LLMs multimodal comparison
• Audio video text features depression detection
• Multimodal representation strategies mental health