A Study of Dropout-Induced Modality Bias on Robustness to Missing Video Frames for Audio-Visual Speech Recognition
Authors: Yusheng Dai, Hang Chen, Jun Du, Ruoyu Wang, Shihao Chen, Haotian Wang, Chin-Hui Lee

Advanced Audio-Visual Speech Recognition (AVSR) systems have been observed to be sensitive to missing video frames performing even worse than single-modality models. While applying the common dropout techniques to the video modality enhances robustness to missing frames it simultaneously results in a performance loss when dealing with complete data input. In this study we delve into this contrasting phenomenon through the lens of modality bias and uncover that an excessive modality bias towards the audio modality induced by dropout constitutes the fundamental cause. Next we present the Modality Bias Hypothesis (MBH) to systematically describe the relationship between the modality bias and the robustness against missing modality in multimodal systems. Building on these findings we propose a novel Multimodal Distribution Approximation with Knowledge Distillation (MDA-KD) framework to reduce over-reliance on the audio modality maintaining performance and robustness simultaneously. Finally to address an entirely missing modality we adopt adapters to dynamically switch decision strategies. The effectiveness of our proposed approach is evaluated through comprehensive experiments on the MISP2021 and MISP2022 datasets. Our code is available at https://github.com/dalision/ModalBiasAVSR.