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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.
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Authors and Affiliations
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
Yu Zhou & Wen Gao
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100080, China
Xilin Chen & Liang-Guo Zhang
School of Computer Science and Technology, Dalian Maritime University, Dalian, 116026, China
Chunli Wang
- Yu Zhou
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- Wen Gao
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- Xilin Chen
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- Liang-Guo Zhang
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- Chunli Wang
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© 2007 Springer-Verlag Berlin Heidelberg
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Zhou, Y., Gao, W., Chen, X., Zhang, LG., Wang, C. (2007). Signer Adaptation Based on Etyma for Large Vocabulary Chinese Sign Language Recognition. In: Ip, H.HS., Au, O.C., Leung, H., Sun, MT., Ma, WY., Hu, SM. (eds) Advances in Multimedia Information Processing – PCM 2007. PCM 2007. Lecture Notes in Computer Science, vol 4810. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77255-2_59
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