A study on pronunciation assessment of English learners based on temporal classification algorithm

被引：0
作者：
Wang L. [1 ]
机构：
[1] Department of Foreign Languages Teaching and Research, Research Centre for Foreign Language Education and Assessment, Luoyang Normal University, Henan, Luoyang
来源：
Applied Mathematics and Nonlinear Sciences | 2024年 / 9卷 / 01期
关键词：
English pronunciation; Feature extraction; Information gain; Temporal classification; Wavelet feature scale;
D O I：
10.2478/amns.2023.2.00749
中图分类号：
学科分类号：
摘要：
This paper utilizes a time-series classification algorithm to classify samples, and the selected datasets are utilized to calculate the distance of time-series samples. The information gain is calculated using the information obtained, and the information entropy is determined collectively for the node datasets to generate time series features. The convolution operation is used to obtain the formal representation of the time series classification, and the extracted English pronunciation features are adaptively matched. The evaluation results were determined using a multilayer wavelet feature scale transformation method, and English learners achieved scores of 6 and above in all three tests using the time-series classification algorithm. To master standard English pronunciation, English learners should use the temporal classification algorithm. © 2023 Lina Wang, published by Sciendo.
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