In the traditional methods, the automatic scoring system design of oral English computer test mainly included the automatic scoring method of oral English computer test based on spectrum analysis and the automatic scoring method of oral English computer test based on wavelet analysis. The research on related system design methods had attracted great attention. Research on the optimization design method of the automatic scoring system for spoken English testing was of great significance in improving the automatic scoring level of the computer-based oral English test and promoting the construction of the intelligent level of the computer-based English oral test. According to the results of speech recognition, the automatic evaluation of oral English tests based on a computer was realized. In order to improve the objectivity of the computer-based test, it was necessary to design an automatic scoring system for the computer-based English test, combined with the intelligent scoring system for the computer-based English test, to perform speech recognition and semantic feature recognition for the output of the computer-based English test. The computer-assisted oral test (computer-assisted oral test for short) had been gradually applied to various oral tests at all levels, greatly improving the efficiency of test administration.
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However, the manual reviewing machine to assist oral test recording still required a huge labor cost. With the popularization of computers and networks and the improvement of related technical performance, the requirements for listening, speaking, reading, and writing skills in English were getting higher and higher. According to the comprehensive experiment, the automatic scoring result of the system is much higher than that of the traditional method, which greatly improves the recognition ability of oral pronunciation, solves the difference between the automatic scoring of the system and the manual scoring, and promotes the computer automatic scoring system to replace or partially replace the manual marking. Aiming at the reliability of the automatic scoring system, based on the principle of sequence matching, this paper adopts the spoken speech feature extraction method to extract the features of spoken English test pronunciation and establishes a dynamic optimized spoken English pronunciation signal model based on sequence matching, which could maintain good dynamic selection and clustering ability in a strong interference environment. The traditional speech signal processing method only focuses on the extraction of scoring features, which could not ensure the accuracy of the scoring algorithm. With the application of an automatic scoring system to all kinds of oral English tests at all levels, the efficiency of test implementation has been greatly improved.