NDLI: A phone-viseme dynamic Bayesian network for audio-visual automatic speech recognition

Content Provider	IEEE Xplore Digital Library
Author	Terry, L. Katsaggelos, A.K.
Copyright Year	2008
Description	Author affiliation: Dept. of Electr. Eng. & Comput. Sci., Northwestern Univ., Evanston, IL (Terry, L.; Katsaggelos, A.K.)
Abstract	This work extends and improves a recently introduced (Dec. 2007) dynamic Bayesian network (DBN) based audio-visual automatic speech recognition (AV-ASR) 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 doing so, our system makes improvements in word error rate (WER) and overall recognition accuracy in a large vocabulary continuous speech recognition task (LVCSR). The ldquobestrdquo performing proposed system attains a WER of 66.71%whereas the ldquobestrdquo baseline system performs at a WER of 64.30%. The proposed system also improves accuracy to 45.95% from 39.40%.
Starting Page	1
Ending Page	4
File Size	1344320
Page Count	4
File Format	PDF
ISBN	9781424421749
ISSN	10514651
DOI	10.1109/ICPR.2008.4761927
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Automatic speech recognition Hidden Markov models Streaming media Psychology Speech recognition Modems Error analysis Vocabulary Defense industry
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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