Abstract
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. The traditional speech signal processing method only focuses on the extraction of scoring features, which could not ensure the accuracy of the scoring algorithm. 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. 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.
Highlights
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 [1]
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. e correlation statistical feature analysis method was used to design the automatic scoring system of oral English computer test, so as to improve the automatic scoring ability of oral English computer test [4]. e frequency characteristics and phase characteristics of the pronunciation feature sequence of the oral English test were scattered, resulting in poor accuracy and low stability of the system. erefore, it was necessary to optimize the signal processing part of the automatic scoring system of oral
We presented a design of an automatic scoring system for the oral English test based on sequence matching. e algorithm design of the automatic scoring system for the oral English test was carried out by using the speech signal processing method [6], the speech signal collection of oral English was carried out by using the time series analysis method, the collected oral English pronunciation sequence was mixed by using the decision feedback equalization adjustment method, and the statistical information feature of oral English pronunciation sequence was extracted
Summary
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 [1]. E algorithm design of the automatic scoring system for the oral English test was carried out by using the speech signal processing method [6], the speech signal collection of oral English was carried out by using the time series analysis method, the collected oral English pronunciation sequence was mixed by using the decision feedback equalization adjustment method, and the statistical information feature of oral English pronunciation sequence was extracted. E correlation statistical feature analysis method was used to design the automatic scoring system for the oral English computer test, so as to improve the automatic scoring ability of the oral English computer test. In the validity study of the automatic scoring system developed by Orient Company for evaluating the telephone oral test Phonepass SET-10, the total scores of human evaluation and machine evaluation and the correlation coefficients of scores in each dimension were mainly reported [16]. ere was no essential difference in the technical paths adopted by ordinate and SpeechRater scoring systems at the level of speech recognition. e automatic speech recognition programs of the two systems were responsible for processing the original speech files, that is, speech segmentation of vocabulary units and conversion of acoustic spectrum, so as to prepare for feature parameter extraction and score calculation [17]. e speech recognition programs of the two systems were established according to the hidden Markov model (HMM), which can be used to recognize the speech of nonnative speakers
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