English Oral Data Analysis and Feedback System Based on Support Vector Machine

In view of the problems of insufficient evaluation of students' oral ability and delayed feedback in traditional English oral teaching, an English oral data analysis and feedback system based on SVM is introduced to help students improve their oral expression ability through intelligent evaluation and instant feedback. First, an English oral data analysis and feedback system is constructed based on speech recognition module, support vector machine (SVM) analysis model and feedback generation module. The system obtains students' oral input through speech recognition technology and converts it into text data. Then, the support vector machine model is used to conduct multi-dimensional analysis of students' pronunciation, intonation, grammar and fluency to evaluate their oral ability. Finally, based on the analysis results, the system generates a personalized feedback report and provides specific improvement suggestions, such as pronunciation correction, grammar tips and fluency training. Through multiple rounds of data training and optimization, the system can gradually improve its accuracy and track students' learning progress in real time. The experimental results show that the analysis model based on support vector machines can more accurately identify key problems in students' oral English. The improvement of all students' abilities is relatively close, and the improvement of most students is between 12.5% and 14.29%, indicating that the feedback mechanism of the system has a relatively balanced effect on promoting students' oral English ability. The oral data analysis and feedback system based on support vector machine provides an innovative solution for English oral teaching, which not only improves students' oral proficiency, but also promotes personalized learning and autonomous improvement. This system has high practical value and is expected to be widely applied in future English teaching.