Abstract

In today’s society, the continuous deepening of international cultural integration has become the background of the times. China has become more and more closely connected with the world, and many physical or online news media have become a platform for China to receive world information and spread Chinese culture. Business English translation is therefore valued by translation researchers and translators. Aiming at the shortcomings of current business English translation research, this paper designs and develops a business English translation architecture based on artificial intelligence speech recognition and edge computing. First of all, considering the relevance and complementarity between speech and text modalities, this paper uses the deep neural network feature fusion method to effectively fuse the extracted monomodal features and perform speech recognition. Secondly, adopt the edge computing method to establish the business English translation system architecture. Finally, the simulation test analysis verifies the efficiency of the business English translation framework established in this paper. Compared with the existing methods, our proposal improved the accuracy than others at least 10% and the time of model building also decreased obviously. The purpose of this research is to discuss how to deal with the many differences between the source language and the target language, and how to enhance the readability of the translation and meet the reader’s cultural cognition and needs.

Highlights

  • International business activities in China have been increasing for many years

  • In the training process of this experiment, the input of the deep learning model is input by frame, and the dimension of each frame of data is different according to the different feature parameters, and the corresponding output label of each frame of data is obtained after forward propagation

  • In planning scheme 2, after the acoustic model is obtained, the optimal path in the state diagram is selected according to the speech frame and the existing acoustic model, and each frame is matched to the state diagram to obtain the corresponding state of each frame

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Summary

Introduction

International business activities in China have been increasing for many years. Descriptive translation studies (DTS) broadens the horizons of translation studies by shifting the focus from goal orientation to target culture rather than prescriptive translation studies [19, 20] It provides a theoretical framework for corpus-based translation. Aiming at the shortcomings of current business English translation research, this paper designs and develops a business English translation architecture based on artificial intelligence speech recognition and edge computing. We design and develop a business English translation architecture based on artificial intelligence speech recognition and edge computing to deal with the many differences between the source language and the target language, and to enhance the readability of the translation and meet the reader’s cultural cognition and needs.

Business English Translation Framework
Edge Computing Technology
Research on Business English Translation Based on Speech Recognition
Case Study of Business English Translation
Application Effect Evaluation
Findings
Conclusion

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