Application of Speech Recognition in English PronunciationCorrection

Abstract

In the context of economic globalization, countries are beginning to conduct ordinary trade transactions, so English, asthe most frequently used language, has been incorporated into the education system of each government and studentsare being taught English systematically, but the fastest way to improve English must be for a student to have a one-onone lesson with a native English speaker. However, with the world’s advanced educational resources, teaching Englishto students on a one-to-one basis is obviously not practical. Therefore, implanting a speech recognition system intoeducational software to help the software track or correct students’ English learning in real time is possible. However,replacing a real English teacher with the current speech recognition accuracy rate is still a challenge. Therefore, thispaper explains in detail how to improve speech recognition accuracy through lip motion tracking and MFCC and to givelearners the corresponding vocabulary ratings and suggestions for improvement through this model. In the long run, theknowledge tracking model is used to help students find the most suitable learning style.

Deep Learning Backward Recognition Model to Improve Pronunciation Accuracy in the Teaching of Spoken English

“…Additionally, deep learning techniques enable the development of voice-enabled virtual tutors and language learning platforms that engage learners in immersive spoken English practice sessions. As deep learning continues to advance, it holds immense potential to further enhance speech recognition capabilities and revolutionize spoken English teaching, facilitating more effective and engaging language learning experiences for students worldwide [16].…”