On the use of perceptual Line Spectral Pairs Frequencies for speaker identification

Line Spectral Pairs Frequencies (LSFs) provide an alternative representation of the linear prediction coefficients. In this paper an investigation is carried out for extracting feature for speaker identification task which is based on perceptual analysis of speech signal and LSF. A modified version of the standard perceptual analysis is applied to obtain better performance. We have extracted the conventional LSF from the perceptually modified speech signal. State-of-the art Gaussian Mixture Model (GMM) based classifier is employed to design the closed set speaker identification system. The proposed method shows significant performance improvement over existing techniques in three different speech corpuses.