Design of Regression Model for MultiParameter Evaluation of English Pronunciation Quality

Abstract

In order to better evaluate the quality of English pronunciation, this paper proposes a regression model design method based on multiparameter evaluation of English pronunciation quality. This method takes college students’ English pronunciation as the research object. Through the combination of various algorithms, a speech recognition model based on a machine learning neural network is constructed with characteristic parameters as input, and a speech quality evaluation model based on multiple regression is constructed with multiparameters such as intonation, speed, rhythm, and intonation as evaluation indicators. The experimental results show that log posterior probability and GOP are good measures of pronunciation standard. When used alone, a higher correlation with manual scores can be obtained, and the correlation of both exceeds 0.5. GOP has the best performance, with a correlation of 0.549. The combination of these two pronunciation standard evaluation features can further improve the evaluation performance, and the correlation degree reaches 0.574. Compared with the GOP algorithm with better performance, the evaluation performance is improved by 4.6%. Conclusion. The model provides a scientific basis for oral English speech recognition and objective evaluation of pronunciation quality.