Abstract: Artificial intelligence speech recognition mostly judges the accuracy of grammar or sentence in the detection of pronunciation error, but has little research on pronunciation judgment, so it cannot effectively correct the pronunciation. This study analyzes the application of image target recognition in English learning task. Task-based approach emphasizes the process of English learning, not the result, the purposeful communication and meaning expression, encourages learners to open their mouths, and emphasizes that English language learning activities and their tasks are realistic in life. In addition, this paper introduces the DNN adaptive technique based on KL divergence regularization to adapt the acoustic model. Finally, this paper uses the experimental contrast method to compare and analyze the algorithm of this research with the traditional algorithm. The research shows that the recognition ability of the algorithm for confusing phonemes is improved than that of traditional algorithms, and this conclusion provides a powerful result for the introduction of error correction algorithms into education networks. By using the platform of autonomous learning center, students can improve their English level by completing the tasks chosen by teachers or by themselves and through training.
Keywords: Deep learning, image target recognition, DNN algorithm, English learning
