Selection of the most relevant online English semantic art translation in cross-lingual information retrieval based on speech signal analysis model Online publication date: Mon, 07-Feb-2022
by Yuji Miao; Yanan Huang
International Journal of Arts and Technology (IJART), Vol. 13, No. 3, 2021
Abstract: In the cross-language information retrieval environment, semantic ontology model matching and feature extraction are needed for semantic translation processing and semantic information analysis. Hence, the efficient model should be designed. There are some semantic conflicts in cross-semantic information retrieval database, which seriously affect the accuracy of language translation and information retrieval. Therefore, it is necessary to design the most relevant semantic translation in cross-language information retrieval. Voice is the most common way of communication so far. In this paper, speech signal analysis and extraction technology is used to improve the accuracy of art cross-language information retrieval. Experimental results show that the retrieval rate of the proposed method is higher than the traditional method. This study combines the art factor with the technology to reach the goal of the comprehensive analysis.
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