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Selection of the most relevant online English semantic art translation in cross-lingual information retrieval based on speech signal analysis model

Selection of the most relevant online English semantic art translation in cross-lingual... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Arts and Technology Inderscience Publishers

Selection of the most relevant online English semantic art translation in cross-lingual information retrieval based on speech signal analysis model

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1754-8853
eISSN
1754-8861
DOI
10.1504/IJART.2021.120761
Publisher site
See Article on Publisher Site

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.

Journal

International Journal of Arts and TechnologyInderscience Publishers

Published: Jan 1, 2021

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