Unsupervised Learning for Speech Motion Editing
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2003Pay-Per-View via TIB Hannover:
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We present a new method for editing speech related facial motions. Our method uses an unsupervised learning technique, Independent Component Analysis (ICA), to extract a set of meaningful parameters without any annotation of the data. With ICA, we are able to solve a blind source separation problem and describe the original data as a linear combination of two sources. One source captures content (speech) and the other captures style (emotion). By manipulating the independent components we can edit the motions in intuitive ways.
BibTeX
@inproceedings {10.2312:SCA03:225-231,
booktitle = {Symposium on Computer Animation},
editor = {D. Breen and M. Lin},
title = {{Unsupervised Learning for Speech Motion Editing}},
author = {Cao, Yong and Faloutsos, Petros and Pighin, Frédéric},
year = {2003},
publisher = {The Eurographics Association},
ISSN = {1727-5288},
ISBN = {1-58113-659-5},
DOI = {10.2312/SCA03/225-231}
}
booktitle = {Symposium on Computer Animation},
editor = {D. Breen and M. Lin},
title = {{Unsupervised Learning for Speech Motion Editing}},
author = {Cao, Yong and Faloutsos, Petros and Pighin, Frédéric},
year = {2003},
publisher = {The Eurographics Association},
ISSN = {1727-5288},
ISBN = {1-58113-659-5},
DOI = {10.2312/SCA03/225-231}
}