College Oral English Teaching Reform Driven by Big Data and Deep Neural Network Technology

Abstract

The ultimate goal of English teaching is to cultivate the students’ ability to communicate information in English, master good language learning methods, and become independent language learners and users. Therefore, successful English language teaching needs to be achieved through language communication training between teachers and students and between students. This article investigates the importance of promoting the reform of oral English teaching in China’s English teaching environment. We believe that to promote the reform of oral English teaching, an oral teaching environment must be available. However, the current common problem in oral English teaching in colleges and universities is that the spoken conversation objects are not standard enough, or there is no person who can talk to. Therefore, an intelligent spoken dialogue system based on big data and neural network technology is particularly important, and the quality of dialogue depends on accurate spoken speech evaluation. We first extracted six features of pronunciation quality, fluency, content richness, topic relevance, grammar, and vocabulary richness. Secondly, we propose an evaluation model that connects specific TDNN layers in a feedforward manner, using the feature representation of target words in different TDNN layers, which can obtain richer context information and greatly reduce the amount of model parameters. Finally, we conducted a simulation experiment. The experimental results show that the proposed model is accurate in evaluating spoken English and can effectively assist the reform of spoken English teaching in colleges and universities, and its performance is better than SVM by 9.2%.