Abstract
The paper aims to establish a effective feature form of visual speech to realize the Chinese viseme recognition. We propose and discuss a representation model of the visual speech which bases on the local binary pattern (LBP) and the discrete cosine transform (DCT) of mouth images. The joint model combines the advantages of the local and global texture information together, which shows better performance than using the global feature only. By computing LBP and DCT of each mouth frame capturing during the subject speaking, the Hidden Markov Model (HMM) is trained based on the training dataset and is employed to recognize the new visual speech. The experiments show this visual speech feature model exhibits good performance in classifying the difference speaking states.
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MeiXia, Z., XiBin, J. (2012). Joint LBP and DCT Model for Visual Speech. In: Luo, J. (eds) Affective Computing and Intelligent Interaction. Advances in Intelligent and Soft Computing, vol 137. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27866-2_13
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DOI: https://doi.org/10.1007/978-3-642-27866-2_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27865-5
Online ISBN: 978-3-642-27866-2
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