{"title":"A Study on the Recognition of English Pronunciation Features in Teaching by Machine Learning Algorithms","authors":"Xiong Wei","doi":"10.5626/jcse.2023.17.3.93","DOIUrl":null,"url":null,"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.","PeriodicalId":37773,"journal":{"name":"Journal of Computing Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computing Science and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5626/jcse.2023.17.3.93","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Engineering","Score":null,"Total":0}
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.
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.