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.

本文探讨了应用可视嘴唇分类器时由于多对一的映射所产生的音素之间的歧义，证明了不同的音素到可视嘴唇映射之间的性能差异，提出了一种新的算法用于构建标记语音数据的音素到可视嘴唇映射，并且表明这种新的可视嘴唇 'Bear' 映射的性能优于以前的单位。