English speech sound improvement system based on deep learning from signal processing to semantic recognition

Abstract

With the global integration and the increasing level of China's internationalization, the demand of Chinese people for English learning is growing rapidly. At the same time, English has gradually become one of the most frequently used languages in the world economic exchanges and cultural information exchanges. After the material life demands of the members of the domestic society are fully satisfied, the learning and explanation of the corresponding knowledge content of the English language are gradually highly valued by the staff in various production fields. After AI proposed deep learning models, researchers also began to improve these models and use them in speech recognition. Through multi-layer nonlinear structure, the essential features are preserved and more abstract advanced features are extracted, so the recognition rate is improved. Computer-aided language learning will change the existing language teaching mode and learning environment, so that learners can learn independently anytime and anywhere. Deep learning can give learners accurate, objective and timely pronunciation evaluation and feedback guidance. It can also help learners find out the differences between their pronunciation and standard pronunciation through repeated listening and comparison, correct their pronunciation errors, and improve the efficiency of language learning. This paper constructs an English language improvement system based on deep learning. The experimental results show that the proposed method can effectively analyze the input speech signals and make corresponding feedback.