  Speech recognition is a challenging problem. Due to the acoustic limitations,
using visual information is essential for improving the recognition accuracy in
real-life unconstraint situations. One common approach is to model the visual
recognition as nonlinear optimization problem. Measuring the distances between
visual units is essential for solving this problem. Embedding the visual units
on a manifold and using manifold kernels is one way to measure these distances.
This work is intended to evaluate the performance of several manifold kernels
for solving the problem of visual speech recognition. We show the theory behind
each kernel. We apply manifold kernel partial least squares framework to OuluVs
and AvLetters databases, and show empirical comparison between all kernels.
This framework provides convenient way to explore different kernels.
