Application of entertainment interactive robot based on speech recognition in English artificial intelligence teaching evaluation and automatic feedback

With the development of intelligent voice and interactive robot technology, new technologies have built a virtual E-learning learning environment that can provide students with an immersive learning experience, making this new learning mode more entertaining. This article investigates the application of entertainment interactive robots based on speech recognition in English artificial intelligence teaching evaluation and automatic feedback. The system has constructed an oral evaluation model based on deep reinforcement learning, which learns the optimal behavioral strategies through interaction with the environment. The model will train through oral conversations with learners to learn how to accurately evaluate oral proficiency and provide relevant feedback. After the construction of the system is completed, the accuracy and efficiency of the system are improved by adjusting the parameters of the model, increasing the diversity of training data, and improving the user interface and interaction mode based on user feedback, making it more friendly and easy to use. The experimental results show that the English oral evaluation and automatic feedback system designed in this paper based on deep reinforcement learning and speech recognition algorithms has high accuracy and efficiency. The system can accurately evaluate learners' oral proficiency and provide personalized learning suggestions based on individual differences.