  This paper presents a novel deep learning architecture for word-level
lipreading. Previous works suggest a potential for incorporating a pretrained
deep 3D Convolutional Neural Networks as a front-end feature extractor. We
introduce a SpotFast networks, a variant of the state-of-the-art SlowFast
networks for action recognition, which utilizes a temporal window as a spot
pathway and all frames as a fast pathway. We further incorporate memory
augmented lateral transformers to learn sequential features for classification.
We evaluate the proposed model on the LRW dataset. The experiments show that
our proposed model outperforms various state-of-the-art models and
incorporating the memory augmented lateral transformers makes a 3.7%
improvement to the SpotFast networks.
