With the continuous development and popularization of Internet technology, online English learning system has become the first choice of many students and educational institutions. However, pronunciation accuracy has always been an important challenge for online English learning systems. The traditional pronunciation classification method has some problems, such as low accuracy and low computational efficiency. To solve this problem, deep confidence network and support vector machine are integrated to construct a pronunciation classification error detection model. The results showed that the loss value of the model tended to be stable after 869 iterations, indicating a good learning effect. The bilingual evaluation BLEU value reached 0.85, indicating a high degree of agreement between the model evaluation and the manual evaluation. The accuracy rate of 91.69% and the F1 value of 0.83 proved the high efficiency of the model in pronunciation classification and error detection. The model achieved 100% feature recognition accuracy in 206 iterations, demonstrating its ability to quickly learn and adapt to pronunciation features. The excellent performance of these performance indicators directly improves the accuracy of pronunciation training in online English learning systems. It also proves that the model is a powerful tool for more effective application of speech recognition and artificial intelligence technology in the field of education, thereby improving learning efficiency.
Rong Zhang (Sat,) studied this question.