Abstract
The use of n-grams is quite prevalent in the field of pattern recognition. In this paper, we use this concept to build new feature vectors from extracted parameters to be used for visual speech classification. We extract the lip contour using edge detection and connectivity analysis. The boundary is defined using six cubic curves. The visual parameters are used to build n-gram feature vectors. Two sets of classification experiments are performed with the n-gram feature vectors: using the hidden Markov model and using multiple data mining algorithms in WEKA, a tool widely used by researchers. Preliminary results show encouraging results.
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Singh, P., Laxmi, V., Gupta, D., Gaur, M.S. (2010). Lipreading Using n–Gram Feature Vector. In: Herrero, Á., Corchado, E., Redondo, C., Alonso, Á. (eds) Computational Intelligence in Security for Information Systems 2010. Advances in Intelligent and Soft Computing, vol 85. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16626-6_9
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DOI: https://doi.org/10.1007/978-3-642-16626-6_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16625-9
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