Oral English training system based on LSTM network

With the acceleration of globalization, spoken English has become an important tool for people to communicate. However, the traditional methods of oral English training often lack of individuation and intelligence, and it is difficult to meet the needs of learners. To solve this problem, this study proposes a oral English training system based on LSTM (Long Short-Term Memory) network. The system uses the memory and context sensing ability of LSTM network to model and analyze spoken English, and realizes the automatic recognition, evaluation and feedback of spoken English pronunciation. This study verifies the effectiveness and practicability of the system through experiments. The system can not only provide learners with personalized oral training programs, but also help them correct pronunciation errors in time and improve their oral English.