An intelligent hybrid–integrated system using speech recognition and a 3d display for early childhood education

Abstract

In the past few years, people’s attitudes toward early childhood education (PAUD) have undergone a complete transformation. Personalized and intelligent communication methods are highly praised, which also promotes the further focus on timely and effective human–computer interaction. Since traditional English learning that relies on parents consumes more time and energy and is prone to errors and omissions, this paper proposes a system based on a convolution neural network (CNN) and automatic speech recognition (ASR) to achieve an integrated process of object recognition, intelligent speech interaction, and synchronization of learning records in children’s education. Compared with platforms described in the literature, not only does it shoot objects in the real-life environment to obtain English words, their pronunciation, and example sentences corresponding to them, but also it combines the technique of a three-dimensional display to help children learn abstract words. At the same time, the cloud database summarizes and tracks the learning progress by a horizontal comparison, which makes it convenient for parents to figure out the situation. The performance evaluation of image and speech recognition demonstrates that the overall accuracy remains above 96%. Through comprehensive experiments in different scenarios, we prove that the platform is suitable for children as an auxiliary method and cultivates their interest in learning English.