  In this work, we present the Densely Connected Temporal Convolutional Network
(DC-TCN) for lip-reading of isolated words. Although Temporal Convolutional
Networks (TCN) have recently demonstrated great potential in many vision tasks,
its receptive fields are not dense enough to model the complex temporal
dynamics in lip-reading scenarios. To address this problem, we introduce dense
connections into the network to capture more robust temporal features.
Moreover, our approach utilises the Squeeze-and-Excitation block, a
light-weight attention mechanism, to further enhance the model's classification
power. Without bells and whistles, our DC-TCN method has achieved 88.36%
accuracy on the Lip Reading in the Wild (LRW) dataset and 43.65% on the
LRW-1000 dataset, which has surpassed all the baseline methods and is the new
state-of-the-art on both datasets.
