A Spoken English Teaching System Based on Speech Recognition and Machine Learning

Fengming Jiao, Jiao Song, Xin Zhao, Ping Zhao, Ru Wang


The learning model and environment are two major constraints on spoken English learning by Chinese learners. The maturity of computer-aided language learning brings a new opportunity to spoken English learners. Based on speech recognition and machine learning, this paper designs a spoken English teaching system, and determines the overall architecture and functional modules of the system according to the system’s functional demand. Specifically, MATLAB was adopted to realize speech recognition, and generate a speech recognition module. Combined with machine learning algorithm, a deep belief network (DBN)-support vector machine (SVM) model was proposed to classify and detect the errors in pronunciation; the module also scores the quality and corrects the errors in pronunciation. This model was extended to a speech evaluation module was created. Next, several experiments were carried out to test multiple attributes of the system, including the accuracy of pronunciation classification and error detection, recognition rates of different environments and vocabularies, and the real-timeliness of recognition. The results show that our system achieved good performance, realized the preset design goals, and satisfied the user demand. This research provides an important theoretical and practical reference to transforming English teaching method, and improving the spoken English of learners.

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Copyright (c) 2021 Fengming Jiao, Jiao Song, Xin Zhao, Ping Zhao, Ru Wang

International Journal of Emerging Technologies in Learning (iJET) – eISSN: 1863-0383
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