  Lip reading, also known as visual speech recognition, aims to recognize the
speech content from videos by analyzing the lip dynamics. There have been
several appealing progress in recent years, benefiting much from the rapidly
developed deep learning techniques and the recent large-scale lip-reading
datasets. Most existing methods obtained high performance by constructing a
complex neural network, together with several customized training strategies
which were always given in a very brief description or even shown only in the
source code. We find that making proper use of these strategies could always
bring exciting improvements without changing much of the model. Considering the
non-negligible effects of these strategies and the existing tough status to
train an effective lip reading model, we perform a comprehensive quantitative
study and comparative analysis, for the first time, to show the effects of
several different choices for lip reading. By only introducing some easy-to-get
refinements to the baseline pipeline, we obtain an obvious improvement of the
performance from 83.7% to 88.4% and from 38.2% to 55.7% on two largest public
available lip reading datasets, LRW and LRW-1000, respectively. They are
comparable and even surpass the existing state-of-the-art results.
