Signer adaptation based on etyma for large vocabulary Chinese sign language recognition

Authors:
Yu Zhou;Xilin Chen;Liang-Guo Zhang;Chunli Wang;Wen Gao
Affiliations:
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;School of Computer Science and Technology, Dalian Maritime University, Dalian, China;School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
Venue:
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
Year:
2007

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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.