Automatic Speech Error Detection Algorithm for English Learners Based on Machine Learning

As computer technology continues to advance, machine learning has achieved significant breakthroughs in speech recognition and Natural language processing (NLP). Notably, deep learning technology, with its remarkable feature learning and pattern recognition capabilities, holds vast potential for application in the realm of speech error detection. Focusing on the challenge of automatically detecting phonetic errors made by English learners, this article introduces an algorithm model rooted in deep learning principles. Furthermore, it presents an automatic detection model tailored for English learners' phonetic errors, leveraging machine learning techniques and validated through rigorous experiments. The experimental findings reveal that the proposed algorithm can proficiently pinpoint pronunciation errors committed by English learners and furnish them with precise feedback alongside corrective measures. Compared with traditional speech error detection methods, the automatic speech error detection algorithm based on deep learning has higher accuracy. The application of this algorithm is helpful to improve learners' pronunciation accuracy and oral expression ability, and brings innovation and value to language learning and education.