A Study on the Recognition of English Pronunciation Features in Teaching by Machine Learning Algorithms

Q3 Engineering
Xiong Wei
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引用次数: 0

Abstract

A better understanding of students" English pronunciation features would be a useful guide for teaching spoken English. This paper first analyzed the English pronunciation features and extracted Mel-frequency cepstral coefficients (MFCC) features from the pronunciation signal. Then, the support vector machine (SVM) method was used to identify the cases of incorrect and correct pronunciation. To further improve the recognition effect, deep features were extracted using deep brief network (DBN) as the input of the SVM, and the parameters of both DBN and SVM were optimized by the sparrow search algorithm (SSA). Experiments were conducted on the dataset. The results showed that the MFCC-SSA-SVM algorithm had better recognition performance than the MFCC-SVM algorithm. The DBN-SVM algorithm had higher recognition correctness and accuracy than the MFCC-SSA-SVM algorithm, while the SSA-DBN-SVM method had 88.07% correctness and 85.49% accuracy, indicating the best performance. The results demonstrated the reliability of the proposed method for English pronunciation feature recognition; therefore, it can be applied in practical spoken language teaching.
基于机器学习算法的英语语音特征识别研究
更好地了解学生的英语发音特征将对英语口语教学起到有益的指导作用。本文首先对英语语音特征进行分析,从语音信号中提取Mel-frequency倒谱系数(MFCC)特征。然后,利用支持向量机(SVM)方法识别发音错误和正确的情况。为进一步提高识别效果,采用深度简要网络(deep brief network, DBN)作为支持向量机的输入提取深度特征,并采用麻雀搜索算法(sparrow search algorithm, SSA)对DBN和SVM的参数进行优化。在数据集上进行了实验。结果表明,MFCC-SSA-SVM算法比MFCC-SVM算法具有更好的识别性能。DBN-SVM算法的识别正确性和准确率均高于MFCC-SSA-SVM算法,而SSA-DBN-SVM方法的识别正确性和准确率分别为88.07%和85.49%,性能最好。结果表明,该方法用于英语语音特征识别是可靠的;因此,它可以应用于实际的口语教学中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Computing Science and Engineering
Journal of Computing Science and Engineering Engineering-Engineering (all)
CiteScore
1.00
自引率
0.00%
发文量
11
期刊介绍: Journal of Computing Science and Engineering (JCSE) is a peer-reviewed quarterly journal that publishes high-quality papers on all aspects of computing science and engineering. The primary objective of JCSE is to be an authoritative international forum for delivering both theoretical and innovative applied researches in the field. JCSE publishes original research contributions, surveys, and experimental studies with scientific advances. The scope of JCSE covers all topics related to computing science and engineering, with a special emphasis on the following areas: Embedded Computing, Ubiquitous Computing, Convergence Computing, Green Computing, Smart and Intelligent Computing, Human Computing.
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