Modified MFCCs for robust speaker recognition

Mel-scale frequency cepstrum coefficienls (MFCCs) are commonly used katues in speaker recognition systems, but MFCC values are not very robust in the presence of noise. thus, the modified MFCCs (named as SMN-CMN-MFCC) based on the general noisy speech model is proposed in this paper, which uses spectrum mean normalization (SMN) to suppress the additive noise, and uses cepstral mean normalization (CMN) to remove the effect of convolutional noise. Theoretical analyses show that the combination of SMN and CMN can inhibit additive and convolutional noise at the same time. To verify the performance of the SMN-CMN-MFCC, \\e have conducted some speaker recognition tesls. With the same convolutional noise component, the additive white noise experimenls and the additive factory noise experimenls show that SMN-CMN-MFCC provides 10.5% and 9.6% relative improvement than the conventional MFCC and ΔMFCC features, respectively. ©2010 IEEE.