An Automatic Pronunciation Error Detection and Correction Mechanism in English Teaching Based on an Improved Random Forest Model

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.

AI and AI-powered tools for pronunciation training

“…The difference between "automatic" and "intelligent" seems to lie in the degree of manipulation of the gathered data. Automated tools compare the data against the model and give feedback based on the articulators' incorrect position or quantity of the phoneme (Dai, 2022). The ASR system's main modules are oral language assessment, pronunciation error detection, and corrective feedback.…”

Deep Learning Backward Recognition Model to Improve Pronunciation Accuracy in the Teaching of Spoken English

“…In [21] investigates the impact of automatic speech recognition on the pronunciation and speaking skills of English as a Foreign Language (EFL) learners. In [22] develops a model for evaluating pronunciation quality based on neural networks. In [14] devise a voice recognition-based game to enhance English pronunciation accuracy.…”

Retracted: An Automatic Pronunciation Error Detection and Correction Mechanism in English Teaching Based on an Improved Random Forest Model

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”