Academic Journal of Computing & Information Science, 2023, 6(10); doi: 10.25236/AJCIS.2023.061008.
Department of Basic Courses, Hebei Agricultural University, Huanghua, Hebei, 061100, China
As the most widely used language in the world, English has always had the largest number of learners. Therefore, this study has a practical foundation for the recognition of English stressed syllables. As is well known, listening and speaking are crucial aspects of language learning, as they are directly related to communication. Therefore, this article aimed to design a mature syllable recognition algorithm and assist it based on neural prediction models. In the end, this article used the algorithm system for a month of auxiliary training for a certain English major class, and conducted a comparative test on phrase recognition rate and pronunciation accuracy before and after. The results showed that the phrase recognition rate increased from 89.34% to 96.05%, and the pronunciation accuracy rate increased from 73.65% to 92.84%, comprehensively improving students' English learning ability.
Neural Prediction Model, Convolutional Neural Network, Recognition of Stressed Syllables, Spatiotemporal Neural Network
Qingjiao Lu. Recognition and Evaluation Algorithm for English Pronunciation Syllables Based on Neural Prediction Model. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 10: 48-53. https://doi.org/10.25236/AJCIS.2023.061008.
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