Causality-Guided Context-Aware Multimodal Public Speaking Anxiety Detection for Out-of-Distribution Generalization

Abstract

Public Speaking Anxiety Detection (PSAD) is a complex and challenging task that involves detecting anxiety through diverse multimodal cues. While deep neural networks have achieved remarkable success in this task, their performance tends to degrade significantly under distribution shifts, especially in Out-Of-Distribution (OOD) scenarios. Given the substantial impact of latent contexts in PSAD, it is crucial to explore how contexts influence OOD generalization. Causality plays a fundamental role in uncovering the relationships between multimodal data and labels. Understanding these causal relationships helps tackle the OOD generalization challenge via causal invariance under context-driven shifts. In this work, we propose a novel Context-Aware Multimodal Adversarial Learning (CAMAL) framework to model and debias complex multimodal contexts in PSAD. A dual adversarial learning strategy jointly optimizes the causal independence between multimodal causal features and contexts. Experimental results on two benchmark datasets demonstrate the superiority of our approach over state-of-the-art baselines under distribution shifts.