@inproceedings{Louis2008,
abstract = {This work extends and improves a recently introduced (Dec. 2007) dynamic Bayesian network (DBN) based audio-visual automatic speech recognition (AVASR) system. That system models the audio and visual components of speech as being composed of the same sub-word units when, in fact, this is not psycholinguistically true. We extend the system to model the audio and visual streams as being composed of separate, yet related, sub-word units. We also introduce a novel stream weighting structure incorporated into the model itself In recognition accuracy in a large vocabulary continuous speech recognition task (LVCSR). The "best" performing proposed system attains a WER of 66.71% whereas the "best" baseline system performs at a WER of 64.30%. The proposed system also improves accuracy to 45.95%from 39.40%. {\textcopyright} 2008 IEEE.},
author = {Terry, Louis and Katsaggelos, Aggelos K.},
booktitle = {2008 19th International Conference on Pattern Recognition},
doi = {10.1109/ICPR.2008.4761927},
isbn = {978-1-4244-2174-9},
issn = {1051-4651},
month = {dec},
pages = {1--4},
publisher = {IEEE},
title = {{A phone-viseme dynamic Bayesian network for audio-visual automatic speech recognition}},
url = {http://ieeexplore.ieee.org/document/4761927/},
year = {2008}
}

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