Speaker Identification Using Linear Predictive Cepstral Coefficients And General Regression Neural Network

摘要：

　　A text-independent,closed-set speaker identification method is proposed in this paper.The method uses linear predictive cepstrum coefficients(LPCCs)as the measured features and follows general regression neural network(GRNN)approaches based on non-linear partition(NLP)algorithm and kernel principal component analysis(KPCA).The input speech signal is pre-emphasized,windowed,and LPC analyzed,resulting in a sequence of vectors of LPC derived cepstrum coefficients.To reduce the correlation and dimension of elements in the feature vector,the NLP algorithm is employed to partition the LPCCs into several segments.The dimensions of each LPCCs segment are reduced by KPCA,then fed to a GRNN for the classification of speaker identification.The numerical experiments are carried out to verify the theoretical results and clearly show that our identification system has good recognition ability in term of accuracy.

关键词： General regression neural network,Speaker identification Linear predictive cepstrum coefficients Non-linear partition algorithm

作者: Penghua Li Fangchao Hu Yinguo Li Yang Xu

作者单位: Chongqing Automotive Electronics & Embedded System Research Center,College of Automation,Chongqing University of Posts & Telecommunications,Chongqing 400065

会议类型: 国际会议

会议名称: The 33th Chinese Control Conference第33届中国控制会议

会议地点: 南京

会议语种:英文

页码: 4952-4956

在线出版日期: 2014-07-28（万方平台首次上网日期，不代表论文的发表时间）