It is necessary to study the application of digital technology in English speech feature recognition. This paper combines the actual needs of English speech feature recognition to improve the digital algorithm. Moreover, this paper combines fuzzy algorithm to analyze English speech features, analyzes the shortcomings of traditional algorithms, proposes the fuzzy digitized English speech recognition algorithm, and builds an English speech feature recognition model on this basis. In addition, this paper conducts time-frequency analysis on chaotic signals and speech signals, eliminates noise in English speech features, improves the recognition effect of English speech features, and builds an English speech feature recognition system based on digital means. Finally, this paper conducts grouping experiments by inputting students’ English pronunciation forms and counts the results of the experiments to test the performance of the system. The research results show that the method proposed in this paper has a certain effect.
The purpose of this paper is to combine digital sensing technology with English character recognition in order to improve its overall effectiveness. In addition, this paper will comprehensively analyze the functional requirements of the digital system in teaching management and will clarify the functional goals that the digital system of teaching management needs to achieve. In addition, this study does an in-depth analysis of the many functions of the system and categorizes each of those functions into a number of distinct business operations. In addition, the English classroom teaching mode is improved with the help of machine learning, deep learning, and digital technology. In addition, an English classroom teaching system that is based on IoT Networks technology is constructed in this study. In addition to this, the study investigates the algorithmic flow of the functional structure modules of the system, develops each functional module of the system in detail, and elucidates the software design schemes at all levels connected to each functional module. In conclusion, the experimental research presented in this work validates the efficiency of the algorithm model presented in this paper. The findings of the study indicate that the digital English teaching system constructed in this work is effective, which supports the findings of the study.
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