Design and Application of English Pronunciation Correction Algorithm Based on SVM+LDA

For the common pronunciation errors of English learners in the pronunciation process, an English pronunciation error correction algorithm based on SVM (Support Vector Machine) and LDA (Latent Dirichlet Allocation) is introduced. The algorithm aims to accurately identify and correct the problems in learners' pronunciation, thereby improving their pronunciation accuracy and fluency. By combining the feature extraction of speech signals with machine learning methods, different types of pronunciation errors can be effectively distinguished and corrected. First, the pronunciation data of English learners are collected and preprocessed to extract speech features, including MFCCs (Mel-Frequency Cepstral Coefficients), formants, pitch, etc. Secondly, LDA is used to reduce the dimension of the extracted features to reduce the complexity of the feature space and enhance the ability to distinguish pronunciation errors. Then, SVM is used for classification training to train a pronunciation error model. Finally, the trained model is used to predict the learner's pronunciation, and corresponding pronunciation correction suggestions are made according to the classification results. This method can effectively identify different types of pronunciation errors and provide personalized correction feedback according to the nature of the error. When the pronunciation error correction algorithm based on SVM+LDA is used for testing, the learners' error rate is 31.9% on average before error correction, and drops to 26.3% after error correction. The English pronunciation error correction algorithm based on SVM+LDA introduced in this study can effectively improve the pronunciation accuracy of English learners. This method has strong practicality and scalability, and can provide strong support for English pronunciation teaching and speech recognition technology.