Research on Intelligent English Oral Training System in Mobile Network

Abstract

With the rapid development of mobile network, mobile learning, as a new learning form, is gradually accepted by people. Based on the Android mobile platform, this paper designed a spoken English training system that could be applied to mobile network equipment from the aspects of speech recognition, pronunciation scoring and function setting. Based on the characteristics of the Android system, this paper selected the MEL cepstrum coefficient as the feature parameters to speech recognition, and introduced the dynamic time neat algorithm as the matching algorithm of the speech recognition pattern to make speech recognition more suitable for mobile Internet devices. Besides, the voice formant was used as a reference for oral scores and the scoring method based on single reference template was adopted. Finally, the spoken English training system was developed under the eclipse integration environment. The test results showed that the success rate of voice input was over 98%, and the accuracy rate of spoken voices of monophthong words, diphthong words and polysyllabic words was 97.15%, 94.96% and 93.62% respectively, suggesting that the system could accurately input and score English learners’ spoken English, and assist English pronunciation.