The Role of Artificial Intelligence in Facilitating Real-Time Language Translation to Complement ESL Education

Real-time English translation systems incorporating speech recognition have a wide range of application scenarios due to the need for further language translation support for second language learners in ESL programs. The traditional Transformer model is further improved by this study using the end-to-end speech recognition model for performance enhancement considerations when performing real-time language translation tasks. The study examines the degree to which the real-time language translation system enhances the learning effect of students in ESL courses. It is found that the Transformer model based on the attention mechanism has obvious performance advantages in a large corpus, and the improved Transformer model containing the transcription network module, prediction network module, and cointegration network module has stronger performance in recognizing English speech. In the case study based on five students, the average recognition time of the translation system under the improved Transformer model is 1.2295 seconds, which is 0.8132 seconds faster than that of the traditional Transformer model, proving that it has a better real-time English translation performance. In a controlled experiment of ESL course learning within a school, the average translation score of the students in the experimental group is 90.45±2.91, which is better than the average translation score of the students in the control group, and there is a significant difference in the translation scores between the experimental group and the control group (P<0.001).