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2006 IEEE International Conference on Multimedia and Expo
Acoustically-Driven Talking Face Synthesis using Dynamic Bayesian Networks
Toronto, ON, Canada
July 09-July 12
ISBN: 1-4244-0366-7
ASCII Text
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Jianxia Xue, Jonas Borgstrom, Jintao Jiang, Lynne Bernstein, Abeer Alwan, "Acoustically-Driven Talking Face Synthesis using Dynamic Bayesian Networks," Multimedia and Expo, IEEE International Conference on, pp. 1165-1168, 2006 IEEE International Conference on Multimedia and Expo, 2006.
 
 
BibTex
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@article{ 10.1109/ICME.2006.262743,
author = {Jianxia Xue and Jonas Borgstrom and Jintao Jiang and Lynne Bernstein and Abeer Alwan},
title = {Acoustically-Driven Talking Face Synthesis using Dynamic Bayesian Networks},
journal ={Multimedia and Expo, IEEE International Conference on},
volume = {0},
year = {2006},
isbn = {1-4244-0366-7},
pages = {1165-1168},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICME.2006.262743},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Multimedia and Expo, IEEE International Conference on
TI - Acoustically-Driven Talking Face Synthesis using Dynamic Bayesian Networks
SN - 1-4244-0366-7
SP1165
EP1168
A1 - Jianxia Xue,
A1 - Jonas Borgstrom,
A1 - Jintao Jiang,
A1 - Lynne Bernstein,
A1 - Abeer Alwan,
PY - 2006
KW - null
VL - 0
JA - Multimedia and Expo, IEEE International Conference on
ER -
 
Jianxia Xue, University of California, Los Angeles, CA 90095, USA. jxue@ee.ucla.edu
Jonas Borgstrom, University of California, Los Angeles, CA 90095, USA. jonas@ee.ucla.edu
Jintao Jiang, House Ear Institute, Los Angeles, CA 90057, USA. jjiang@hei.org
Lynne Bernstein, House Ear Institute, Los Angeles, CA 90057, USA. lbernstein@hei.org
Abeer Alwan, University of California, Los Angeles, CA 90095, USA. alwan@ee.ucla.edu
Dynamic Bayesian Networks (DBNs) have been widely studied in multi-modal speech recognition applications. Here, we introduce DBNs into an acoustically-driven talking face synthesis system. Three prototypes of DBNs, namely independent, coupled, and product HMMs were studied. Results showed that the DBN methods were more effective in this study than a multilinear regression baseline. Coupled and product HMMs performed similarly better than independent HMMs in terms of motion trajectory accuracy. Audio and visual speech asynchronies were represented differently for coupled HMMs versus product HMMs.
Citation:
Jianxia Xue, Jonas Borgstrom, Jintao Jiang, Lynne Bernstein, Abeer Alwan, "Acoustically-Driven Talking Face Synthesis using Dynamic Bayesian Networks," icme, pp.1165-1168, 2006 IEEE International Conference on Multimedia and Expo, 2006
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