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J. Hefei Univ. Tech. (Nat. Sci.)

合肥工业大学学报(自然科学版)

JOURNAL OF HEFEI UNIVERSITY OF TECHNOLOGY (NATURAL SCIENCE)

Competent Authorities: Ministry of Education of the People's Republic of China

Sponsor by: Hefei University of Technology

ISSN 1000-1646, CN 34-1001/N

A word order conversion method based attention mechanism for sign language

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基于注意力机制的手语语序转换方法

Journal Information

J. Hefei Univ. Tech. (Nat. Sci.), Jan.2023, Vol.46 No.1: 42-46, 59

DOI: 10.3969/j.issn.1003-5060.2023.01.007

Authors

ZHANG Zheyan, WANG Qingshan

(School of Mathematics, Hefei University of Technology, Hefei 230601, China)

Abstract and Keywords

Abstract: Considering the differences in grammar and language structure between hearing-impaired and able-bodied people, this paper designs a sign language order converter based on attention mechanism to realize the conversion of sign language order to written expression. In the encoding stage, the word order converter uses bidirectional long short-term memory (LSTM) to extract the features of the sign language order, and in the decoding stage, it uses one-dimensional convolution to extract the features of the hidden state of the encoder. In addition, the attention mechanism is used to avoid the long-distance dependency problem, so as to obtain the written expression. The results show that the highest accuracy of the word order converter is 92.64%.

Keywords: attention mechanism; word order conversion; encoder-decoder model; feature extraction

作者信息

张哲岩，王青山

(合肥工业大学数学学院，安徽合肥230601)

摘要和关键词

摘要: 文章考虑听障人士与健全人士在语法和语言结构上的差异，设计一种基于注意力机制的手语语序转换器，实现手语语序到书面表达的转换。语序转换器在编码阶段使用双向长短期记忆网络（long short-term memory，LSTM）提取手语语序特征，解码阶段使用一维卷积提取编码器隐藏状态的特征，并利用注意力机制避免了长距离的依赖问题，从而得到书面表达。实验结果表明，语序转换器准确率最高为92.64%。

关键词: 注意力机制；语序转换；编解码模型；特征提取

Funding

国家自然科学基金资助项目（61571179）

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