Design and Implementation of an English Pronunciation Scoring System for Pupils Based on DNN-HMM
Hongyang Wang, Jie Xu, Hai Ge, Yufeng Wang · 2019
Nowadays, the problem of poor performance on English pronunciation among Chinese pupils is widespread because of the shortage of foreign teachers, related products and resources. To improve their English pronunciation, we have developed an English word pronunciation scoring system. The system applies DNN-HMMs (deep neural network-hidden Markov models) for acoustic modeling to implement the English speech recognition and the GOP (Goodness of Pronunciation) algorithm for scoring pronunciation based on the likelihood output by DNN-HMMs. The method is implemented on HTK (Hidden Markov Model Toolkit), which integrates a set of tools for building speech recognition system. Moreover, the system is practically deployed, in which the server side conducts the model training; the client side collects pupils' speech data, and feedbacks pupils the inferred scores. Specifically, we collect 150 word pronunciations of 30 pupils as test data and compare the scores given by the system with scores given by teachers. The results demonstrate the reliability of the developed pupil pronunciation scoring system based on DNN-HMM.