关键词: 韵律特征, 高斯混合模型（GMM）超矢量, 支持向量机, 文本无关说话人确认

Abstract: A text-independent speaker verification method based on prosodic features and SVM model is proposed.With wavelet analysis，prosodic features are extracted from MFCC，F0 and energy contours respectively，these complementary features are fused at feature level to yield a most effective feature PMFCCFE，GMM mean supervectors of PMFCCFE are used to train SVM models to discriminates target speakers and imposters more effectively.The experiments conducted on the 2006 NIST 8side-1side subset show that the prosodic GMM-SVM system relatively improves the performance of the verification system by 57.9% in EER，41.4% in MinDCF，compared with the MFCC-based GMM-UBM system.

Key words: prosodic features, Gaussian Mixture Model（GMM） supervector, Support Vector Machine（SVM）, text-independent speaker verification