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  What we’re speaking about at this time falls underneath Visual Speech Recognition (VSR) research - that is, using solely visible
  means to know what's being conveyed. In the early days of VSR research, datasets like AVLetters needed to be
  hand-labeled and -categorized, a labor-intensive limitation that severely restricted
  the quantity of data accessible for coaching machine learning models.
  
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  - with today’s state-of-the-artwork systems
  achieving well over 95 % sentence-degree word accuracy. "The size and quality of the database determine the coaching effect of this model, and a perfect database may even promote the discovery and answer of increasingly more complicated and tough problems in lipreading duties." Other obstacles
  can embrace environmental elements like poor lighting and shifting backgrounds which
  may confound machine vision programs, as can variances due the speaker’s
  skin tone, the rotational angle of their head (which shifts
  the considered angle of the mouth) and the obscuring presence of wrinkles and beards.
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