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Lip Movement Synthesis from Speech Based on Hidden Markov Models

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dc.contributor.author Eli Yamamoto en
dc.contributor.author Satoshi Nakamura en
dc.contributor.author Kiyohiro Shikano en
dc.date.accessioned 2012-08-22T07:58:37Z en
dc.date.available 2012-08-22T07:58:37Z en
dc.date.issued 1998-04 en
dc.identifier.isbn 0818683449 en
dc.identifier.uri http://hdl.handle.net/10061/7889 en
dc.description AFGR 1998: .IEEE International Conference on Automatic Face and Gesture Recognition, April 14-16, 1998, Nara, Japan. en
dc.description.abstract Speech intelligibility can be improved by adding lip image and facial image to speech signal. Thus the lip image synthesis plays an important role to realize a natural human-like face of computer agents. Moreover the synthesized lip movement images can compensate lack of auditory information for hearing impaired people. We propose a novel lip movement synthesis method based on mapping from input speech based on Hidden Markov Model (HMM). This paper compares the HMM-based method and a conventional method using vector quantization (VQ). In the experiment, error and time differential error between synthesized lip movement images and original ones are used for evaluation. The result shows that the error of the HMM based method is 8.7% smaller than that of the VQ-based method. Moreover, the HMM-based method reduces time differential error by 32% than the VQ's. The result also shows that the errors are mostly caused by phoneme /h/ and /Q/. Since lip shapes of those phonemes are strongly dependent on succeeding phoneme, the context dependent synthesis on the HMM-based method is applied to reduce the error. The improved HMM-based method realizes reduction of the error (differential error) by 10.5% (11%) compared with the original HMM-based method en
dc.language.iso en en
dc.rights Copyright 1998 IEEE en
dc.title Lip Movement Synthesis from Speech Based on Hidden Markov Models en
dc.type.nii Conference Paper en
dc.textversion Publisher en
dc.identifier.spage 154 en
dc.identifier.epage 159 en
dc.relation.doi 10.1109/AFGR.1998.670941 en
dc.identifier.NAIST-ID 73296626 en


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