Intelligent English teaching prediction system based on SVM and heterogeneous multimodal target recognition

With the development of speech recognition technology, human services in many fields have gradually been replaced by intelligent voice services. Natural language processing technology and speech recognition technology are gradually becoming the key technologies in human-computer interaction systems. However, there are still some problems in intelligent speech recognition. In view of this, this study uses English as the basic language to carry out research and combines support vector machine to construct intelligent English recognition and predic-tion system to obtain EEG signals related to Chinese speech. Moreover, this paper adopts wavelet packet decomposition and co-space mode to extract the features of the acquired EEG signals and uses the support vector machine model to classify and compare the signal features. The research results show that the recognition rate of the traditional algorithm is significantly lower than the speech recognition rate of the algorithm model, and the algorithm of this study basically meets the requirements of the actual use of the system.