English speech sound improvement system based on deep learning from signal processing to semantic recognition
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English speech sound improvement system based on deep learning from signal processing to semantic recognition Yucheng Yang1 · Yibo Yue1 Received: 1 March 2020 / Accepted: 6 July 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
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. Computeraided 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. Keywords Deep Learning · Confidence · English teaching · Speech quality · Speech recognition · Human–computer interaction
1 Introduction In recent years, with the development of deep learning, big data and cloud computing technology, speech recognition and evaluation technology has been developed rapidly. Deep learning (DL) originates from the field of machine learning and aims to build and simulate the deep neural network (DNN) of human brain for analysis and learning. DNN has shown outstanding advantages in solving some complex problems, because it can simulate the neurons of human brain to carry out multi-layer depth transmission to interpret data, which has been verified in the field of speech recognition. Especially with the rapid development of graphics calculator and cloud computing technology, the computing complexity of DNN is no longer a problem. Therefore, the
* Yibo Yue [email protected] 1
Shaoyang University, Shaoyang 422000, Hunan, China
study of English speech recognition technology based on deep learning can greatly improve the ability of speech information processing, improve the efficiency of information acquisition, and get a better user experience (Yao and Ye 2012
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