Software Simulation in English Translation Platform Based on Gaussian Hybrid Model

Abstract

In this article, we will first try to create a general development platform for embedded systems. The goal of this step is to establish an experimental platform that can support various peripheral modules and can be reused. The connection between the modules can be reconfigured to meet the different needs of embedded research and learning. On this basis, the system uses the audio frequency spectrum program as an input function to represent the dispersion pattern of signal energy, which can be tested with characteristic nodes simulated by convolutional neural network software. In addition, the auxiliary neural network software simulation core can continuously learn the detailed characteristics of the audio frequency spectrum, making it easy to recognize environmental sounds. In addition, the sound signal and the neural cycle are related in time. The neural network can study the relationship between different frames in the time domain to compensate for the defects caused by the complex neural network in modeling time series. Finally, this article focuses on the process of building an English translation platform based on the mobile cloud data model. The client is targeted at the Android platform, while the server is based on the laaS system. According to the model of mobile cloud data processing, the calculation of mobile phones in computer-intensive programs is studied. Through the hardware design and distribution of the system, we are able to use mobile cloud technology as a desktop-intensive program to solve the problem of effectiveness in the solution. The article promotes the development of an English translation platform by applying the research results of environmental sound recognition based on embedded system software simulation to the design of the English translation platform.

Research on Digital Transformation of English Translation Teaching Mode in Colleges and Universities under the Background of Internet Plus Education

“…Of particular concern is the improvement of resource quality and teacher participation in resource development, which have respectively increased resource quality by 30% and student satisfaction by 35%. These measures will help universities better meet the needs of digital education, improve the quality of English translation education and student satisfaction [10] .…”