Signer Adaptation Based on Etyma for Large Vocabulary Chinese Sign Language Recognition

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.

• Type: Conference
• Year: 2007
• Where: PCM
• Authors: Yu Zhou, Wen Gao, Xilin Chen, Liang-Guo Zhang, Chunli Wang
• Keywords: Large Vocabulary, Multimedia, PCM 2007, Signer Adaptation, Signer Independent Slr