Design of English pronunciation quality evaluation system based on the deep learning model
Author:
Zhang Fangfang1, Zhou Zhihong2
Affiliation:
1. 1 School of International Economics and Trade, Hunan Sany Polytechnic College , Changsha , , China . 2. 2 School of Mechanical Engineering, Hunan Sany Polytechnic College , Changsha , , China .
Abstract
Abstract
To explore the design of the English pronunciation quality evaluation system, a design of the English pronunciation quality evaluation system based on a deep learning model is proposed. This method explores the research of English pronunciation quality evaluation by recommending key technical problems and solutions based on information represented by the deep learning model. The research shows that the efficiency of the English pronunciation quality evaluation system based on a deep learning model is about 30% higher than that of traditional methods. Through the experimental verification, the English pronunciation quality evaluation model method is reasonable and reliable. It can give learners timely, accurate, and objective evaluation and feedback guidance, help learners find out the difference between their pronunciation and standard pronunciation, correct pronunciation errors, and improve the efficiency of English spoken language learning.
Publisher
Walter de Gruyter GmbH
Subject
Applied Mathematics,Engineering (miscellaneous),Modeling and Simulation,General Computer Science
Reference27 articles.
1. Chen, W., Wang, W., Wang, K., Li, Z., & Liu, S. (2020). Lane departure warning systems and lane line detection methods based on image processing and semantic segmentation: a review. Journal of Traffic and Transportation Engineering (English Edition), 7(6), 748-774. 2. Qiu, X., Chen, S., Li, R., Wang, D., & Lin, X. (2021). A post-processing method for text detection based on geometric features. IEEE Access, PP(99), 1-1. 3. Algabri, M., Mathkour, H., Bencherif, M. A., Alsuliman, M., & Mekhtiche, M. A. (2020). Towards deep object detection techniques for phoneme recognition. IEEE Access, PP(99), 1-1. 4. Seujski, M., Suzic, S., Pekar, D., Smirnov, A., & Nosek, T. (2020). Speaker/style-dependent neural network speech synthesis based on speaker/style embedding. Journal of Universal Computer Science, 26(4), 434-453. 5. Park, C., Song, H., & Lee, C. (2020). S 3-net: SRU-based sentence and self-matching networks for machine reading comprehension. ACM Transactions on Asian and Low-Resource Language Information Processing, 19(3), 1-14.
|
|