Speaker identification using linear predictive cepstral coefficients and general regression neural network

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.