A Convolutional Network-Based Intelligent Evaluation Algorithm for the Quality of Spoken English Pronunciation

Xia Zhan

doi:10.1155/2022/7560033

Abstract

Aiming at the problems of long time consumption and low accuracy of traditional spoken English pronunciation quality assessment algorithms, a convolutional network-based intelligent assessment algorithm for spoken English pronunciation quality is proposed. The convolutional neural network structure is given, the original data of the spoken English pronunciation voice signal are collected by multisensor detection, and the spoken English pronunciation voice signal model is constructed. Based on audio and convolutional neural network learning and training, it realizes the feature selection and classification recognition of spoken English pronunciation. The PID algorithm is used to extract the emotional elements of spoken English at different levels to achieve accurate assessment of the quality of spoken English pronunciation. The experimental results show that the average correct rate of spoken English pronunciation of the algorithm in this paper is 94.58%, the pronunciation quality score is 8.52–9.18, and the detection time of 100 phrases is 2.4 s.

Highlights

As a widely used language, English has attracted more and more people’s attention
With the development of speech signal processing technology, the use of speech signal recognition methods to intelligently evaluate the quality of spoken English, combined with speech information processing technology to improve the quality of spoken English pronunciation, is of great significance in improving the effectiveness of spoken English teaching. e intelligent assessment of spoken English pronunciation quality evaluates and calculates pronunciation quality and detects pronunciation errors [2]. e related intelligent assessment algorithm of spoken
Aiming at the problems of the above methods, this paper proposes an intelligent assessment algorithm for spoken English pronunciation quality based on convolutional networks

Summary

Xia Zhan

E convolutional neural network structure is given, the original data of the spoken English pronunciation voice signal are collected by multisensor detection, and the spoken English pronunciation voice signal model is constructed. Based on audio and convolutional neural network learning and training, it realizes the feature selection and classification recognition of spoken English pronunciation. E PID algorithm is used to extract the emotional elements of spoken English at different levels to achieve accurate assessment of the quality of spoken English pronunciation. E experimental results show that the average correct rate of spoken English pronunciation of the algorithm in this paper is 94.58%, the pronunciation quality score is 8.52–9.18, and the detection time of 100 phrases is 2.4 s

Introduction

Output Output Output Output

Number of phrases