Using HMMs in audio-to-visual conversion

One emerging application which exploits the correlation between audio and video is speech driven facial animation. The goal of speech driven facial animation is to synthesize realistic video sequences from acoustic speech. Much of the previous research has implemented this audio to visual conversion strategy with existing techniques such as vector quantization and neural networks. We examine how this conversion process can be accomplished with hidden Markov models.