  Lip-reading aims to infer the speech content from the lip movement sequence
and can be seen as a typical sequence-to-sequence (seq2seq) problem which
translates the input image sequence of lip movements to the text sequence of
the speech content. However, the traditional learning process of seq2seq models
always suffers from two problems: the exposure bias resulted from the strategy
of "teacher-forcing", and the inconsistency between the discriminative
optimization target (usually the cross-entropy loss) and the final evaluation
metric (usually the character/word error rate). In this paper, we propose a
novel pseudo-convolutional policy gradient (PCPG) based method to address these
two problems. On the one hand, we introduce the evaluation metric (refers to
the character error rate in this paper) as a form of reward to optimize the
model together with the original discriminative target. On the other hand,
inspired by the local perception property of convolutional operation, we
perform a pseudo-convolutional operation on the reward and loss dimension, so
as to take more context around each time step into account to generate a robust
reward and loss for the whole optimization. Finally, we perform a thorough
comparison and evaluation on both the word-level and sentence-level benchmarks.
The results show a significant improvement over other related methods, and
report either a new state-of-the-art performance or a competitive accuracy on
all these challenging benchmarks, which clearly proves the advantages of our
approach.
