Research on Multifractal Spectrum Cluster and Its Application in Speaker Recognition

Speech is a complicated nonlinear signal, so traditional speaker recognition technology based on the linear theory is difficult to be further improved. Hence, the multifractal spectrum cluster analytical method is proposed, and applied to the analysis of nonlinear characteristic of the speech signal in speaker recognition of short speech. Through extensive experiments for Cantor sets, it is found that sub-scaling ranges, which were neglected by traditional multifractal method, actually reflected the growth pattern in different growth stages. Therefore, in order to fully consider the fractal characteristics contained in different scaling range, the multifractal spectrum cluster analytical method is proposed to describe the multi-level fractal characteristics accurately and comprehensively. Then, according to the characteristic of the speech signal, an extraction method of speaker multifractal spectrum cluster feature (MSCF) is proposed, which could combine with short-term spectral feature in feature layer effectively. Finally, the combinations of several nonlinear features and short-term spectral feature are applied to speaker recognition. Experiment results based on the TIMIT show that nonlinear feature and short-term spectral feature are highly complementary, which make the error rate of speaker recognition system decrease obviously, especially the combination of MSCF, MFCC and LPC can reduce the error rate to 0.8% in short speech speaker recognition.