关键词: 方言辨识, Mel频率倒谱系数, 滑动差分倒谱特征, 支持向量机

Abstract: MFCC（Mel Frequence Cepstral Coefficients） only reflects speech static feature so its dialect recognition rate is low, while SDC（Shifted Delta Cepstra） reflects speech dynamic feature because of considering the connections between several speech frames. For combination of static and dynamic features, MFCC and SDC extracted from Mandarin, Shanghai dialect, Cantonese, Minnan dialect are employed as the feature vector with SVM（Support Vector Machine） for the dialect identification, and effects on performance of different parameters for SDC are studied. Simulation results demonstrate that the dialect recognition rate with static and dynamic features can be up to 92.5%, but its increase is based on the cost of the working time.

Key words: dialect identification, Mel Frequence Cepstral Coefficients（MFCC）, Shifted Delta Cepstra（SDC）, Support Vector Machine（SVM）