Nonlinear scale decomposition based features for visual speech recognition

A mathematical morphology based filter structure called a sieve is used to process mouth image sequences of a talker's mouth and form visual speech features. The effects of varying the type of filter, the post-processing and hidden Markov model (HMM) parameters on recognition accuracy are investigated using two audio-visual speech databases.