  Several training strategies and temporal models have been recently proposed
for isolated word lip-reading in a series of independent works. However, the
potential of combining the best strategies and investigating the impact of each
of them has not been explored. In this paper, we systematically investigate the
performance of state-of-the-art data augmentation approaches, temporal models
and other training strategies, like self-distillation and using word boundary
indicators. Our results show that Time Masking (TM) is the most important
augmentation followed by mixup and Densely-Connected Temporal Convolutional
Networks (DC-TCN) are the best temporal model for lip-reading of isolated
words. Using self-distillation and word boundary indicators is also beneficial
but to a lesser extent. A combination of all the above methods results in a
classification accuracy of 93.4%, which is an absolute improvement of 4.6% over
the current state-of-the-art performance on the LRW dataset. The performance
can be further improved to 94.1% by pre-training on additional datasets. An
error analysis of the various training strategies reveals that the performance
improves by increasing the classification accuracy of hard-to-recognise words.
