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Inter-frame contextual modelling for visual speech recognition

Inter-frame contextual modelling for visual speech recognition

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Original languageEnglish
Title of host publication2010 17th IEEE International Conference on Image Processing, ICIP 2010
Subtitle of host publicationProceedings
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages93-96
Number of pages4
ISBN (Print)9781424479948
DOIs
StatePublished - 2010
Event17th IEEE International Conference on Image Processing - Hong Kong, Hong Kong

Conference

Conference17th IEEE International Conference on Image Processing
Abbreviated titleICIP 2010
CountryHong Kong
CityHong Kong
Period26/09/1029/09/10
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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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