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Abstract

A method of speech driven lip synthesis which applies viseme based training models to units of visual speech. The audio data is grouped into a smaller number of visually distinct visemes rather than the larger number of phonemes. These visemes then form the basis for a Hidden Markov Model (HMM) state sequence or the output nodes of a neural network. During the training phase, audio and visual features are extracted from input speech, which is then aligned according to the apparent viseme sequence with the corresponding audio features being used to calculate the HMM state output probabilities or the output of the neutral network. During the synthesis phase, the acoustic input is aligned with the most likely viseme HMM sequence (in the case of an HMM based model) or with the nodes of the network (in the case of a neural network based system), which is then used for animation.

Other References

• Chen et al ("Audio-Visual Integration in Multimodal Communication," IEEE Proceedings vol. 86 No. 5, May 1998).
• Goldschen et al ("Rationale for Phoneme-Viseme Mapping and Feature Selection in Visual Speech Recognition", Aug. 28-Sep. 8, 1995