AI Recognition Method of Pronunciation Errors in Oral English Speech with the Help of Big Data for Personalized Learning

Abstract

At present, there is a lack of careful consideration in the judgment process of pronunciation errors in many English speeches. These pronunciation errors will create a great impact on personalized learning. The process of creating a data set for errors is also not an easy work. On considering the above obstacle, an artificial intelligent recognition method of pronunciation errors in English speeches for personalized learning along with big data is proposed. This method takes the average pronunciation level of standard speech as the basis of pronunciation error judgment, and judges the pronunciation and application of words such as speed, pronunciation, semantics, etc. In the Hidden Markov Model (HMM) modelling method of speech recognition, Viterbi algorithm and improved posterior probability algorithm are implemented to recognize student’s vocalization instinctively. Through the segmentation and scoring of basic units, English learners are provided with reliable pronunciation information feedback, correct pronunciation errors and give corresponding feedback according to the judgment results. The innovation outcome establishes that the intelligent recognition method for personalized learning can efficiently diminish the error rate and enhance the accuracy of error detection. Let the artificial intelligence (AI) correct English learner’s pronunciation errors intelligently.