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research
Inter-Frame Contextual Modelling For Visual Speech Recognition
Authors
Philip Hanna
Ming Ji
+3 more
Adrian Pass
Darryl Stewart
Jianguo Zhang
Publication date
1 January 2010
Publisher
Doi
Cite
Abstract
In this paper, we present a new approach to visual speech recognition which improves contextual modelling by combining Inter- Frame Dependent and Hidden Markov Models. This approach captures contextual information in visual speech that may be lost using a Hidden Markov Model alone. We apply contextual modelling to a large speaker independent isolated digit recognition task, and compare our approach to two commonly adopted feature based techniques for incorporating speech dynamics. Results are presented from baseline feature based systems and the combined modelling technique. We illustrate that both of these techniques achieve similar levels of performance when used independently. However significant improvements in performance can be achieved through a combination of the two. In particular we report an improvement in excess of 17% relative Word Error Rate in comparison to our best baseline system. © 2010 IEEE
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University of Dundee Online Publications
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University of Dundee Online Publications
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University of Dundee Online Publications
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University of Dundee Online Publications
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Last time updated on 18/03/2023