Convolutional neural network for substantiation of children's books, intelligent interactive communication and application analysis of voice question answering

Abstract

At present, there are many children accompanying robots. A key indicator to measure their performance is convenient and efficient human–computer interaction. The implementation of voice question-answering technology needs to solve three problems: voice recognition, the establishment of a knowledge base, and answer matching. As the front-end data entry, speech recognition accuracy is directly related to the answer-matching effect of the back-end question-answering system. Based on convolutional neural network and intelligent communication technology, this paper analyzes the characteristics of children's reading robot and designs and develops intelligent voice interaction function on the Android platform. The focus of this paper is on Chinese word segmentation and keyword extraction and matching in the interaction function. Further, we designed a simple question-and-answer database for the children's reading robot. In this paper, convolutional neural network connection timing is used as the feature parameter. Through a large number of experiments, the structure of the CNN network is constantly adjusted to minimize the word error rate, optimize the model recognition rate, and better identify users' voice problems.