Unsupervised adaptive sign language recognition based on hypothesis comparison guided cross validation and linguistic prior filtering

doi:10.1016/j.neucom.2014.08.032

	Unsupervised adaptive sign language recognition based on hypothesis comparison guided cross validation and linguistic prior filtering
	Zhou, Yu 1; Yang, Xiaokang 2; Zhang, Yongzheng 1; Xu, Xiang 1; Wang, Yipeng 1; Chai, Xiujuan 3; Lin, Weiyao 2
	2015-02-03
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
卷号	149 页码:1604-1612
摘要	Signer adaptation is important for sign language recognition systems because a fixed system cannot perform well on all kinds of signers. In supervised signer adaptation, the labeled adaptation data must be collected explicitly. To skip the data collecting process in signer adaptation, we propose a novel unsupervised adaptation method, namely the hypothesis comparison guided cross validation method. The method not only addresses the problem of the overlap between the data set to be labeled and the data set for adaptation, but also employs an additional hypothesis comparison step to decrease the noise rate of the adaptation data set. We also utilize linguistic prior knowledge to down sample the adaptation data list to further decrease the noise rate. To evaluate the effectiveness of the proposed method, the CASIIE-SL-Database is formed, which is the first specialized data set for unsupervised signer adaptation to the best of our knowledge. Experimental results show that the proposed method can achieve relative word error rate reductions of 3.93% and 4.05% respectively compared with self-teaching method and cross validation method. Though the method is proposed for signer adaptation, it can also be applied to speaker adaptation and writer adaptation directly. (C) 2014 Elsevier B.V. All rights reserved.
关键词	Signer adaptation Cross validation Unsupervised learning Sign language recognition
DOI	10.1016/j.neucom.2014.08.032
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61303170] ; National Natural Science Foundation of China[61402472] ; National Natural Science Foundation of China[61303261] ; National Natural Science Foundation of China[61471235] ; National High Technology Research and Development Program of China (863 programs)[2013AA014703] ; National High Technology Research and Development Program of China (863 programs)[2012AA012803]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000356105100049
出版者	ELSEVIER SCIENCE BV
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/9672
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhang, Yongzheng
作者单位	1.Chinese Acad Sci, Inst Informat Engn, Beijing, Peoples R China 2.Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Shanghai 200030, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Zhou, Yu,Yang, Xiaokang,Zhang, Yongzheng,et al. Unsupervised adaptive sign language recognition based on hypothesis comparison guided cross validation and linguistic prior filtering[J]. NEUROCOMPUTING,2015,149:1604-1612.
APA	Zhou, Yu.,Yang, Xiaokang.,Zhang, Yongzheng.,Xu, Xiang.,Wang, Yipeng.,...&Lin, Weiyao.(2015).Unsupervised adaptive sign language recognition based on hypothesis comparison guided cross validation and linguistic prior filtering.NEUROCOMPUTING,149,1604-1612.
MLA	Zhou, Yu,et al."Unsupervised adaptive sign language recognition based on hypothesis comparison guided cross validation and linguistic prior filtering".NEUROCOMPUTING 149(2015):1604-1612.