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. 2022 Jun 23:2022:2501693.
doi: 10.1155/2022/2501693. eCollection 2022.

Multi-Feature Intelligent Oral English Error Correction Based on Few-Shot Learning Technology

Fengxiang Zhang  1 , Jieli Sun  2
Affiliations

Multi-Feature Intelligent Oral English Error Correction Based on Few-Shot Learning Technology

Fengxiang Zhang et al. Comput Intell Neurosci. .

Abstract

The computer-aided language teaching system is maturing thanks to the advancement of few-shot learning technologies. In order to support teachers and increase students' learning efficiency, more computer-aided language teaching systems are being used in teaching and examinations. This study focuses on a multifeature fusion-based evaluation method for oral English learning, completely evaluating specific grammar, and assisting oral learners in improving their oral pronunciation skills. This study proposes an improved method based on HMM a posteriori probability scoring, in which the only standard reference model is no longer used as the basis for scoring and error determination, and instead, the average level of standard pronunciation in the entire corpus is introduced as another judgment basis, based on a preliminary study of speech recognition technology, scoring methods, and relevant theoretical knowledge of information feedback. This strategy can reduce the score limitation caused by standard pronunciation personal differences, lower the system's misjudgment rate in detecting pronunciation errors, and improve the usefulness of error correction information. An expert opinion database has been created based on the most prevalent forms of spoken pronunciation problems, which can successfully assist learners improve their spoken English level by combining the database's corrected information. Finally, this study proposes an artificial scoring system for spoken English that performs activities such as identification, scoring, error judgment, and correction opinion feedback, among others. Finally, it has been demonstrated through trials and tests that adding the average pronunciation level to the system improves the system's scoring performance and has a certain effect on increasing users' oral pronunciation level.

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Conflict of interest statement

The authors do not have any possible conflicts of interest.

Figures

Figure 1
Figure 1
Fractal dimension characteristics of stressed syllables obtained by polymorphic coverage method.
Figure 2
Figure 2
Scoring process based on HMM.
Figure 3
Figure 3
Multifeature intelligent scoring model.
Figure 4
Figure 4
Comparison between score results and average pronunciation level.
Figure 5
Figure 5
Comparison of the number of process runs.
Figure 6
Figure 6
Comparison of detection performance.
Figure 7
Figure 7
Comparison of capture accuracy.
See this image and copyright information in PMC

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