  We are at an exciting time for machine lipreading. Traditional research
stemmed from the adaptation of audio recognition systems. But now, the computer
vision community is also participating. This joining of two previously
disparate areas with different perspectives on computer lipreading is creating
opportunities for collaborations, but in doing so the literature is
experiencing challenges in knowledge sharing due to multiple uses of terms and
phrases and the range of methods for scoring results.
  In particular we highlight three areas with the intention to improve
communication between those researching lipreading; the effects of
interchanging between speech reading and lipreading; speaker dependence across
train, validation, and test splits; and the use of accuracy, correctness,
errors, and varying units (phonemes, visemes, words, and sentences) to measure
system performance. We make recommendations as to how we can be more
consistent.
