Implementation of English Translator Speech Recognition System Function

As a complex technology, speech recognition has entered the stage of large-scale application after more than half a century of development, but there are still challenges such as low recognition rate and slow recognition speed. In view of these problems, this paper delves into the algorithms of speech signal preprocessing, framing and windowing, endpoint detection and feature extraction, and constructs an English translator speech recognition system. The development status of speech recognition technology, as well as some new methods based on deep learning and neural networks, are introduced in related work. In the method part, this paper proposes a four-module language recognition system, including a recognition module, a front-end module, an acoustic module and an execution module, and describes in detail the process of speech sampling and preprocessing. This paper uses the feature extraction method to extract the voice data of the Common Voice open source database, builds an English translator, adopts pattern recognition and automated control methods, and controls it through the Internet and the Internet of Things to realize the network modular design of the English translator and improve the integration capabilities of the English translator.