Speech Function Design and Human-Computer Interaction System Framework of an English Teaching Robot

This As the world's most widely spoken language, English has become a popular topic of research for better teaching models. The study introduces a Deep Belief Network (DBN) algorithm for the speech recognition system improvement, and designs the speech function and human-computer interaction of the teaching robot for better application in real-life English teaching. The results showed that in the performance verification of DBN, the maximum test root mean square error of the DBN model was reduced by 0.67% compared to the ReLU belief network. Compared to the Discrete Hidden Markov Model (DHMM), its recognition rates have been improved by 8.33% and 10.07%, respectively. In addition, in experiments testing the effectiveness of the educational robot's English teaching function, the robot-taught students improved their pronunciation indicators by 14 and 10 points respectively compared to traditional teaching, and also improved their speech speed by 22 and 13 points respectively compared to traditional teaching. This indicates that the teaching robot incorporating the DBN algorithm has excellent performance and provides an effective technical support for the improvement of modern English teaching models.