  Lip-reading has attracted a lot of research attention lately thanks to
advances in deep learning. The current state-of-the-art model for recognition
of isolated words in-the-wild consists of a residual network and Bidirectional
Gated Recurrent Unit (BGRU) layers. In this work, we address the limitations of
this model and we propose changes which further improve its performance.
Firstly, the BGRU layers are replaced with Temporal Convolutional Networks
(TCN). Secondly, we greatly simplify the training procedure, which allows us to
train the model in one single stage. Thirdly, we show that the current
state-of-the-art methodology produces models that do not generalize well to
variations on the sequence length, and we addresses this issue by proposing a
variable-length augmentation. We present results on the largest
publicly-available datasets for isolated word recognition in English and
Mandarin, LRW and LRW1000, respectively. Our proposed model results in an
absolute improvement of 1.2% and 3.2%, respectively, in these datasets which is
the new state-of-the-art performance.
