School of Foreign Languages, Hefei Normal University, Hefei 230601, China.
Comput Intell Neurosci. 2022 Jan 27;2022:8241241. doi: 10.1155/2022/8241241. eCollection 2022.
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.
长期以来，我国许多高校的大学英语教学都侧重于培养学生的阅读能力，而忽视了对学生口语能力的培养，致使许多非英语专业的学生经过多年的英语学习后仍然无法用英语进行交流。本文概述了英语口语智能纠错系统的整体设计与开发过程，重点介绍了英语口语考试、题库管理和评分等功能的实现方法。提出了一种基于 SE（样本熵）和 MFCC（梅尔频率倒谱系数）的多特征融合语音情感识别方法。它表示 SE 非线性动态系统生成新数据的速率。它可以用来描述语音信号对各种情绪的动态波动。为了处理 SE 及其统计信息以及 MFCC，并计算它们属于六种情绪之一的概率，可以使用支持向量机。根据实验评估，本文描述的英语口语识别算法在许多指标上都有明显的性能提升。
