基于自适应GMM阶数与混合特征的说话人识别研究OA

Aiming at the problems of Gaussian mixture model(GMM)order selection defects and insufficient speaker feature information,a speaker recognition algorithm based on adaptive GMM order and fusion of multiple speech features has been proposed.Firstly,the Meier frequency cepstral coefficients(MFCC)and linear prediction Meier frequency cepstrum coefficient(LPMFCC)are extracted,and the 17-dimensional mixed feature parameter combination of MFCC and LPMFCC parameters is obtained according to the Fisher criterion,in order to enhance the feature information of the speaker.Then the sum of squared errors(SSE)in the cluster is calculated in the K-means clustering algorithm according to the adaptive idea.Lastly,the K value is adaptively adjusted by the elbow law to obtain an optimal GMM order,so that the system can obtain the optimal recognition effect under the existing voiceprint features.The results show that the algorithm not only improves the feature information of the speaker,but also overcomes the defects of GMM order selection.And,the hybrid feature LPMFCC+MFCC algorithm is obtained by fusing the two feature algorithms of LPCC and MFCC,whose recognition rate is increased by 26.34%and 12.34%respectively compared with LPCC and MFCC.