Research on Multifeature Intelligent Correction of Spoken English

For a long time, college English teaching in many Chinese universities has focused on cultivating students' reading abilities while ignoring the cultivation of students' speaking abilities, leaving many non-English majors unable to communicate in English even after years of English study. This paper outlines the entire design and development process for an intelligent correction system for spoken English, with a focus on the methods for implementing the functions of spoken English examination, question bank management, and marking. A multifeature fusion of SE (sample entropy) and MFCC (Mel frequency cepstrum coefficient) based speech emotion recognition method is proposed. It denotes the rate at which the SE nonlinear dynamic system generates new data. It can be used to describe the dynamic fluctuation of speech signals in response to various emotions. To process SE and its statistics, as well as MFCC, and calculate the probability that they belong to one of six emotions, the support vector machine is used. The spoken English recognition algorithm described in this paper has obvious performance improvements in many indicators, according to the experimental evaluation.