7. A computer implemented method, comprising: under control of one or more computing systems configured with executable instructions, deriving video feature parameters for an input speech using a refined Gaussian Mixture Model (GMM), the refining comprising: using a minimum generation error (MGE) process to weigh an audio space of the GMM and a video space of the GMM with separate weight parameters; and applying a generalized probabilistic descent (GPD) algorithm to minimize a conversion error of a maximum likelihood estimation (MLE)-based conversion process; and generating facial movement that represents visual characteristics of the input speech based on the refined GMM.
7. A computer implemented method, comprising: under control of one or more computing systems configured with executable instructions, deriving video feature parameters for an input speech using a refined Gaussian Mixture Model (GMM), the refining comprising: using a minimum generation error (MGE) process to weigh an audio space of the GMM and a video space of the GMM with separate weight parameters; and applying a generalized probabilistic descent (GPD) algorithm to minimize a conversion error of a maximum likelihood estimation (MLE)-based conversion process; and generating facial movement that represents visual characteristics of the input speech based on the refined GMM. 14. The computer implemented method of claim 7 , wherein the video feature parameters include static feature parameters and dynamic feature parameters, the dynamic feature parameters being represented as a linear transformation of the static feature parameters.