  Visemes are the visual equivalent of phonemes. Although not precisely
defined, a working definition of a viseme is "a set of phonemes which have
identical appearance on the lips". Therefore a phoneme falls into one viseme
class but a viseme may represent many phonemes: a many to one mapping. This
mapping introduces ambiguity between phonemes when using viseme classifiers.
Not only is this ambiguity damaging to the performance of audio-visual
classifiers operating on real expressive speech, there is also considerable
choice between possible mappings. In this paper we explore the issue of this
choice of viseme-to-phoneme map. We show that there is definite difference in
performance between viseme-to-phoneme mappings and explore why some maps appear
to work better than others. We also devise a new algorithm for constructing
phoneme-to-viseme mappings from labeled speech data. These new visemes, `Bear'
visemes, are shown to perform better than previously known units.
