Affiliations: School of Foreign Languages, Western China Language Service & International Communication Research Center for The Belt and Road Initiative, Chongqing Technology and Business University, Chongqing, China
Correspondence:
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Corresponding author. Hongmei Zhu, School of Foreign Languages, Western China Language Service & International Communication Research Center for The Belt and Road Initiative, Chongqing Technology and Business University, Chongqing, China. E-mail: hmzhu66@163.com.
Abstract: English speech recognition system is affected by a variety of interference factors. Associating the algorithm with the support of modern computer technology can increase the model effect of speech recognition system. Based on the study of the current mainstream controlled natural language thesaurus, this paper proposes a controlled natural language vocabulary classification type. Moreover, this paper defines the domain thesaurus according to the WordNet knowledge description framework, and uses WordNet’s synonym, antisense, upper and lower, etc. In this way, the controlled natural language system can use the semantic relationship of WordNet to identify the words of the non-domain thesaurus input by the user and map the non-domain definition words to the words in the domain thesaurus, thereby improving the ease of use of controlled natural language systems. In addition, this paper designed a controlled experiment to analyze the performance of this system. The research results show that the model constructed in this paper has certain significant effects.
