Affiliations: [a] Basic Teaching Department, Luxun Academy of Fine Arts, Dalian, Liaoning, China | [b] School of Foreign Languages, Hubei University of Automotive Engineering, Shiyan, Hubei, China
Correspondence:
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Corresponding author: Dan Feng. School of Foreign Languages, Hubei University of Automotive Engineering, Shiyan, Hubei 442002, China. E-mail: [email protected].
Abstract: In order to improve the security and performance of the oral English instant translation model, this paper optimizes the instant translation model through the Internet of Things (IoT) security technology and deep learning technology. In this paper, the real-time translation model based on deep learning and IoT technology is analyzed in detail to show the application of these two technologies in the real-time translation model, and the related information security issues are discussed. Meanwhile, this paper proposes a method combining deep learning network and IoT technology to further improve the security of instant translation model. The experimental results show that under the optimized model, the parameter upload time is 60 seconds, the aggregation calculation time is 6.5 seconds, and the authentication time is 7.5 seconds. Moreover, the average recognition accuracy of the optimized model reaches 93.1%, and it is superior to the traditional machine translation method in accuracy and real-time, which has wide practical value and application prospects. Therefore, the research has certain reference significance for improving the security of the English corpus oral instant translation model.
Keywords: Information security, Internet of Things technology, deep learning, oral instant translation model, English corpus
