The Modular Design of an English Pronunciation Level Evaluation System Based on Machine Learning

Abstract

To improve the accuracy of English pronunciation level evaluation, we study the modularization of the English pronunciation level evaluation system unfolding based on machine learning. The S3C2440A chip is used as the main processor of the system, and the spoken English recordings are sent to the evaluation module through the speech upload module. In the evaluation module, the pronunciation signal is filtered by the multilayer wavelet feature scale transformation method, and the intonation, speed, pitch, rhythm, and emotion features are decomposed and extracted. The test results show that the misjudgment rate of different mispronunciations is less than 1% when the system is used to evaluate the English pronunciation level, which proves that it has high evaluation accuracy. In-depth study of speech recognition related theories, speech scoring, and pronunciation correction algorithms are discussed, and an assisted learning system based on AP scoring method and pronunciation resonance peak comparison technology is proposed for the problem of inaccurate pronunciation scoring and lack of effective feedback of speech recognition technology applied to oral learning. The English pronunciation training system has achieved the expected pronunciation following of English phonetic symbols and words, real-time pronunciation. The English pronunciation training system has achieved all basic functions such as pronunciation following, real-time pronunciation evaluation, and pronunciation correction of English phonemes and works as expected. After testing, the system has achieved high accuracy in pronunciation scoring, and the similarity with experts’ scoring is over 90% for vowel and word pronunciation; the efficiency of pronunciation correction reaches 80%, which can improve learners’ pronunciation level to a certain extent.