ABSTRACT Automatic speaker recognition can achieve a high level of performance in matched training and testing conditions. However, such performance drops significantly in mismatched noisy conditions. Recent research indicates that a new speaker feature, gammatone frequency cepstral coefficients (GFCC), exhibits superior noise robustness to commonly used mel-frequency cepstral coefficients (MFCC). To gain a deep understanding of the intrinsic robustness of GFCC relative to MFCC, we design speaker identification experiments to systematically analyze their differences and similarities. This study reveals that the nonlinear rectification accounts for the noise robustness differences primarily. Moreover, this study suggests how to enhance MFCC robustness, and further improve GFCC robustness by adopting a different time-frequency representation.
The contrasts of MFCC and gammatone frequency cepstral coefficients (GFCC) have been compared, and the noise robust improvements by GFCC has been explained in REF .
Analyzing noise robustness of MFCC and GFCC features in speaker identification
2013 IEEE International Conference on Acoustics, Speech and Signal Processing
2013 IEEE International Conference on Acoustics, Speech and Signal Processing
Computer Science