This paper proposes a phonetic feature extraction and recognition model in Korean pronunciation exercises based on AdaBoot moments. Feature extraction algorithms have a great influence on speech emotion recognition algorithms, among which Mel-frequency Cepstral Coefficients (MFCC) is the most commonly used feature in speech emotion recognition. The feature extraction method and the influence of SVM kernel function and parameter selection on the recognition result are studied, and the existing speech feature extraction algorithms and their respective advantages and disadvantages are analyzed, as well as the influence of different kernel functions, kernel parameters and penalty parameters on the recognition performance. An improved grid optimization method is used to further improve the recognition time of voice information.