Design of a Speaking Training System for English Speech Education using Speech Recognition Technology

Abstract

A good English speaking training system can provide an aid to the learning of English. This paper briefly introduced the English speaking training system and described the speaking training scoring and pronunciation resonance peak display modules in the system. The speaking training scoring module scored pronunciation with the Long Short-Term Memory (LSTM). The pronunciation resonance peak display module extracted the resonance peak with Fourier transform and visualized it. Finally, the speaking scoring module, the pronunciation resonance peak display module, and the effect of the whole system in improving students' speaking pronunciation was tested. The results showed that the LSTM-based speaking scoring algorithm had highest scoring accuracy than pattern matching and the recurrent neural network (RNN) algorithm, and its accuracy was 95.21% when scoring the LibriSpeech dataset and 90.12% when scoring the local English dataset. The pronunciation resonance peak display module displayed the change of mouth shape before and after training, and the pronunciation after training was closer to the standard pronunciation. The P value in the comparison of the speaking level before and after training with the system was 0.001, i.e., the difference was significant, which indicated that the students' English speaking proficiency significantly improved.

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

“…In [19] proposes an English speech recognition system model that incorporates computer-aided functions and neural network algorithms. In [20] explores an algorithm for detecting and recognizing English pronunciation in teaching contexts through cluster analysis and improved SSD. In [21] investigates the impact of automatic speech recognition on the pronunciation and speaking skills of English as a Foreign Language (EFL) learners.…”