基于注意力机制的手语语序转换方法

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%。

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%.