计算机与现代化Issue(6)：16-19,4.DOI:10.3969/j.issn.1006-2475.2013.06.005
基于CCA和PCA的说话人特征降维研究
Research on Dimension Reduction of Speaker's Characteristics Based on CCA and PCA
摘要
Abstract
With the purpose of improving the performance of speaker recognition,a method of dimension reduction in speaker's characteristics by jointing CCA and PCA is proposed.Firstly,LPC characteristics based on acoustic models and MFCC characteristics based on auditory models are blended by CCA method so as to enhance the correlativity between LPC and MFCC.After that the PCA method is used to eliminate redundant characteristics so as to reduce the effective characteristic dimensions of speech signal.Experiments show that the efficiency of dimension reduction of this novel method that joints CCA and PCA is significantly improved comparing to that of traditional methods while only using CCA dimension reduction,PCA dimension reduction or manual dimension reduction.关键词
说话人识别/典型相关分析/主成分分析/高斯混合模型/特征降维/线性预测系数/美尔频率倒谱系数Key words
speaker recognition/canonical correlation analysis (CCA)/principal components analysis (PCA)/Gaussian mixture model (GMM)/dimensional reduction/linear prediction coefficient (LPC)/Mel frequency cepstrum coefficient(MFCC)分类
信息技术与安全科学引用本文复制引用
陈觉之,张贵荣,周宇欢..基于CCA和PCA的说话人特征降维研究[J].计算机与现代化,2013,(6):16-19,4.基金项目
江苏省自然科学基金资助项目(BK2009059) （BK2009059）
解放军理工大学预研基金资助项目(2009TX08) （2009TX08）
