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There is a strong correlation between the building blocks of speech (phonemes) and the building blocks of visual speech (visimes). In this paper, this correlation is exploited and an approach is proposed for synthesizing the visual representation of speech from a narrow-band acoustic speech signal. The visual speech is represented in terms of the facial animation parameters (FAPs), supported by the MPEG-4 standard. The main contribution of this paper is the development of a correlation hidden Markov model (CHMM) system, which integrates independently trained acoustic HMM (AHMM) and visual HMM (VHMM) systems, in order to realize speech-to-video synthesis. The proposed CHMM system allows for different model topologies for acoustic and visual HMMs. It performs late integration and reduces the amount of required training data compared to early integration modeling techniques. Temporal accuracy experiments, comparison of the synthesized FAPs to the original FAPs, and audio-visual automatic speech recognition (AV-ASR) experiments utilizing the synthesized visual speech were performed in order to objectively measure the performance of the system. The objective experiments demonstrated that the proposed approach reduces time alignment errors by 40.5% compared to the conventional temporal scaling method, that the synthesized FAP sequences are very similar to the original FAP sequences, and that synthesized FAP sequences contain visual speechreading information that can improve AV-ASR performance.