Hidden Markov Models for Visual Speech Synthesis in Limited Data
This work presents a new approach for estimating control points (facial locations that control movement) to allow the artificial generation of video with apparent mouth movement (visual speech) time-synced with recorded audio. First, Hidden Markov Models (HMMs) are estimated for each visual speech category (viseme) present in stored video data, where a category is defined as the mouth movement corresponding to a given sound and where the visemes are further categorized as trisemes (a viseme in the context of previous and following visemes). Next, a decision tree is used to cluster and relate states in the HMMs that are similar in a contextual and statistical sense. The tree is also used to estimate HMMs that generate sequences of visual speech control points for trisemes not occurring in the stored data. An experiment is described that evaluates the effect of several algorithm variables, and a statistical analysis is presented that establishes appropriate levels for each variable by minimizing the error between the desired and estimated control points. The analysis indicates that the error is lowest when the process is conducted with three-state left-to right no skip HMMs trained using short-duration dynamic features, a high log-likelihood threshold, and a low outlier threshold. Also, comparisons of mouth shapes generated from the artificial control points and the true control points (estimated from video not used to train the HMMs) indicate that the process provides accurate estimates for most trisemes tested in this work. The research presented here thus establishes a useful method for synthesizing realistic audio-synchronized video facial features.