Abstract
Sign language recognition (SLR) with large vocabulary and signer independency is valuable and is still a big challenge. Signer adaptation is an important solution to signer independent SLR. In this paper, we present a method of etyma-based signer adaptation for large vocabulary Chinese SLR. Popular adaptation techniques including Maximum Likelihood Linear Regression (MLLR) and Maximum A Posteriori (MAP) algorithms are used. Our approach can gain comparative results with that of using words, but we only require less than half data. © Springer-Verlag Berlin Heidelberg 2007.
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CITATION STYLE
Zhou, Y., Gao, W., Chen, X., Zhang, L. G., & Wang, C. (2007). Signer adaptation based on etyma for large vocabulary chinese sign language recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4810 LNCS, pp. 458–461). Springer Verlag. https://doi.org/10.1007/978-3-540-77255-2_59
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