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

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.