Abstract
The paper presents a novel feature extraction method to improve the performance of speaker identification systems. The proposed feature has the form of a typical conventional feature, Mel frequency cepstral coefficients (MFCC), but a flexible segmentation to reduce spectral mismatch between training and testing processes. Specifically, the length and shift size of the analysis frame are determined by a pitch synchronous method, pitch synchronous MFCC (PSMFCC). To verify the performance of the new feature, we measure the cepstral distortion between training and testing and also perform closed set speaker identification tests. With text-independent and text-dependent experiments, the proposed algorithm provides 44.3% and 26.7% relative improvement, respectively.