AV-RISE: Hierarchical Cross-Modal Denoising for Learning Robust Audio-Visual Speech Representation

Audio-visual speech recognition (AVSR) leverages complementary visual cues to improve speech recognition. However, in real-world scenarios, both modalities may suffer from noise or occlusion. In such scenarios, most existing fusion strategies overlook the variation in modality-specific quality under different degradation conditions. This limitation may lead to dominance of corrupted modality in the fusion process, resulting in worse AVSR performance than unimodal systems, termed as Corrupted Modality Bias (CMB) in this work. To address this, a self-supervised speech representation learning framework, called AV-RISE, is proposed to employ teacher-student self-distillation to robustly reconstruct clean speech representations from corrupted audio-visual inputs. A hierarchical fusion mechanism is designed to progressively refine audio and visual representations by integrating the Suppression and Enhancement Interaction (SEI) module into each layer of the pre-trained encoder. In the SEI module, cross-modal suppression and modality-oriented enhancement are performed to mitigate noise-induced feature inconsistencies, which strengthens the modeling of complementary semantic representations. Extensive experiments on the LRS2 and LRS3 datasets demonstrate that AV-RISE outperforms SOTA AVSR models, especially under extreme degradation. Most importantly, the hierarchical SEI-based fusion effectively enhances reliable semantic representations to mitigate CMB, by evaluating feature similarities between clean and noise samples.