Influencing Neutrosophic Factors of Speech Recognition Technology in English Collection

1. Introduction

In the past 10 years, the research of neural networks has made great progress and successfully solved many modern problems. He shows excellent intelligence characteristics for practical problems that computers are difficult to solve. With the improvement of speech recognition performance, the popularization and application of speech recognition technology in mobile Internet terminal equipment has been promoted. Due to more application requirements, the development of speech recognition technology based on deep neural networks has accelerated development. In recent years, the application of speech recognition technology has become more and more extensive, and how to improve the robustness of the speech recognition system has attracted the attention of researchers. In real life, factors such as speech sounds and emotions have a great impact on the robustness of the speech recognition system, and the neutrosophic performance of the natural speech recognition system becomes very unstable. Deep neural network has achieved excellent learning ability and high stability in the fields of speech synthesis, information classification, etc. It is the current research hotspot of speech recognition system based on neural network. This article analyzes the factors affecting the application of deep neural networks in speech recognition. Speech recognition technology can improve the accuracy of speech recognition and the efficiency of speech recognition under the application of deep neural network. Speech recognition based on deep neural network is the only way for speech recognition. Based on the research and analysis of speech recognition system and neural network principles, this paper combines English-related decision trees to complete the design and construction of an English speech recognition system based on hybrid framework and series-frame neural network.