An English pronunciation and intonation evaluation method based on the DTW algorithm

Abstract

In order to solve the problems of single feature dimension and low accuracy in traditional oral English evaluation, this study has used the support vector regression algorithm to achieve the effective integration of different dimension evaluation features. The study comprehensively evaluate the overall reading and pronunciation quality of students. This paper designs an evaluation model of spoken English pronunciation quality based on a dynamic time warping algorithm and introduces the evaluation algorithms of pronunciation standard, fluency, and intonation, respectively. A support vector regression algorithm is used to realize the effective fusion of different dimension evaluation features. In addition, the correlation between machine rating and manual rating was used as an evaluation index to evaluate students' oral pronunciation quality from different dimensions. The model shows superior performance in the pronunciation of both monosyllabic and polysyllabic words, which is beneficial to improving students' oral English learning effect.

Using Speech Recognition Algorithms to Improve Listening Training Effects in Foreign

“…The assessment methods for conventional spelling, effortlessness, and accent, respectively, build a framework for the assessment of spoken English pronouncing quality based on a dynamic temporal warping (DTW) algorithm proposed by Duan and He (2023) [16]. The efficient fusion of several dimension assessment features is achieved by the application of a support vector regression (SVR) technique.…”