四川轻化工大学学报（自然科学版）2024，Vol.37Issue(4)：75-83,9.DOI:10.11863/j.suse.2024.04.09
基于自适应GMM阶数与混合特征的说话人识别研究
Research on Speaker Recognition Algorithm Based on Adaptive GMM Order and Hybrid Features
摘要
Abstract
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.关键词
说话人识别/高斯混合模型/梅尔频率倒谱系数/线性预测梅尔系数/Fisher准则/自适应Key words
speaker recognition/Gaussian mixture model/Meier frequency cepstral coefficients/linear prediction of Meier parameters/Fisher criterion/self-adaption分类
信息技术与安全科学引用本文复制引用
范涛,詹旭..基于自适应GMM阶数与混合特征的说话人识别研究[J].四川轻化工大学学报（自然科学版）,2024,37(4):75-83,9.基金项目
四川省科技厅重点研发项目(2022YFS0554) （2022YFS0554）
