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Please use this identifier to cite or link to this item: https://doi.org/10.1109/MMSP.1997.602607
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dc.titleUsing IHMM's in audio-to-visual conversion
dc.contributor.authorRao R.
dc.contributor.authorMersereau R.
dc.contributor.authorChen T.
dc.date.accessioned2018-08-21T05:13:25Z
dc.date.available2018-08-21T05:13:25Z
dc.date.issued1997
dc.identifier.citationRao R., Mersereau R., Chen T. (1997). Using IHMM's in audio-to-visual conversion. 1997 IEEE 1st Workshop on Multimedia Signal Processing, MMSP 1997 : 19-24. ScholarBank@NUS Repository. https://doi.org/10.1109/MMSP.1997.602607
dc.identifier.isbn0780337808
dc.identifier.isbn9780780337800
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/146421
dc.description.abstractOne 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. In this paper, we examine how this conversion process can be accomplished with hidden Markov models.
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentOFFICE OF THE PROVOST
dc.contributor.departmentDEPARTMENT OF COMPUTER SCIENCE
dc.description.doi10.1109/MMSP.1997.602607
dc.description.sourcetitle1997 IEEE 1st Workshop on Multimedia Signal Processing, MMSP 1997
dc.description.page19-24
dc.published.statepublished
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