Artificial intelligence and machine learning enhance speech evaluation technology for objective analysis. Strengthening the development of English pronunciation evaluation techniques or tools plays an important role in helping learners correct pronunciation errors and improve their oral proficiency, which cannot be ignored. Traditional evaluation methods have subjective limitations and the selection of data information is one-sided. So this study proposes a recognition model that integrates acoustic phonetics features, taking into account the language habits and pronunciation characteristics of different learners. Time warping and support vector regression are introduced to improve the original Gaussian mixture model-hidden Markov model for speech recognition. And a method is designed for detecting easily confused pronunciation errors, achieving effective fusion of evaluation features from different dimensions. The results confirm that the proposed method achieves a maximum rating accuracy of 90.140% in four aspects of English pronunciation quality. The Pearson correlation coefficient value between it and manual scoring approaches 0.934, which is much higher than the comparison algorithm. Its pronunciation fluency and quality level are both good, and its resource consumption is relatively small. This pronunciation evaluation method can provide technical tools for evaluating students’ oral English proficiency and improving teaching quality.
Zhang et al. (Sat,) studied this question.