An Automatic Pronunciation Error Detection and Correction Mechanism in English Teaching Based on an Improved Random Forest Model. (29th May 2022)

• Record Type: Journal Article
• Authors: Dai, Yuhua
• Other Names: Liu Wei Academic Editor.
• Is Part Of: Journal of electrical and computer engineering. Volume 2022(2022)
• Publication Date: 2022-05-29
• Subjects: Computer engineering -- Periodicals; Electrical engineering -- Periodicals; 621.3905
• DOI: 10.1155/2022/6011993
• Languages: English
• ISSNs: 2090-0147

Abstract

Abstract : Teachers in traditional English classes focus more on writing and grammar instruction, while oral language instruction is neglected. In exam-oriented education, most Chinese students can master English written test skills, but only a few students can communicate effectively in English daily. People are progressively realizing that language is a tool for communication and communication in recent years, as the frequency of international exchanges has increased and that language learning should focus on oral language education. However, there are numerous issues with teaching oral English. When students perform individual oral practice after class, for example, they are unable to determine whether their pronunciation is correct. As a result, a computer-assisted study into automatic pronunciation of spoken English has become a viable solution to these issues. However, the present spoken English pronunciation mistake correction model's accuracy and stability have not yet reached an optimal level. Based on this background, this work provides an enhanced random forest model and uses it to detect and correct automatic pronunciation errors in English classes. The improved random forest (RF) algorithm is used to classify and detect whether the learner's pronunciation is correct. Mel cepstral coefficient (MFCC) is used for feature extraction, and principal component analysis (PCA) is used for dimensionality reduction of feature data. The experimental structure demonstrates that by using a combination classification framework based on MFCC, PCA, and RF, the learner's pronunciation difficulty may be resolved. This allows for different error categories to receive feedback corrections.