  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 dropout technique to the video
modality enhances robustness to missing frames, it simultaneously results in a
performance loss when dealing with complete data input. In this paper, we
investigate this contrasting phenomenon from the perspective of modality bias
and reveal that an excessive modality bias on the audio caused by dropout is
the underlying reason. Moreover, we present the Modality Bias Hypothesis (MBH)
to systematically describe the relationship between modality bias and
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 and to maintain 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
and validated through a series of comprehensive experiments using the MISP2021
and MISP2022 datasets. Our code is available at
https://github.com/dalision/ModalBiasAVSR
