  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% sparsity on the LRS3 dataset and
outperform the dense equivalent up to 70% sparsity. We evaluate our 50% sparse
model on 7 different visual noise types and achieve an overall absolute
improvement of more than 2% WER compared to the dense equivalent. Our results
confirm that sparse networks are more resistant to noise than dense networks.
