SparseVSR: Lightweight and Noise Robust Visual Speech Recognition
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Recent advances in deep neural networks have achieved unprecedented success in visual speech recognition. However, there remains substantial disparity between current methods and their deployment in resource-constrained devices. In this work, we explore different magnitude-based pruning techniques to generate a lightweight model that achieves higher performance than its dense model equivalent, especially under the presence of visual noise. Our sparse models achieve state-of-the-art results at 10 outperform the dense equivalent up to 70 model on 7 different visual noise types and achieve an overall absolute improvement of more than 2 confirm that sparse networks are more resistant to noise than dense networks.
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