Factorization for generating a library of mouth shapes

Abstract

A library of mouth shapes is created by separating speaker-dependent and speaker independent variability. Preferably, speaker dependent variability is modeled by a speaker space while the speaker independent variability (i.e. context dependency), is modeled by a set of normalized mouth shapes that need be built only once. Given a small amount of data from a new speaker, it is possible to construct a corresponding mouth shape library by estimating a point in speaker space that maximizes the likelihood of adaptation data and by combining speaker dependent and speaker independent variability. Creation of talking heads is simplified because creation of a library of mouth shapes is enabled with only a few mouth shape instances. To build the speaker space, a context independent mouth shape parametric representation is obtained. Then a supervector containing the set of context-independent mouth shapes is formed for each speaker included in the speaker space. Dimensionality reduction is used to find the areas of the speaker space.

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