Normalization of Modulation Features for Speaker Recognition

features are emerging as a more recent alternative to more conventional magnitude-based features for speech processing applications such as speaker recognition. Frequency modulation (FM) based features are one such example and in this paper their normalization using feature warping is examined in detail. Evaluations of different FM feature and warping configurations on the NIST 2001 Speaker Recognition corpus show that feature warping is not an effective normalization technique for FM features, despite its well-known effectiveness for Mel frequency cepstral coefficients (MFCC). This study further suggests a closer investigation on feature dependency of the existing system when using new features.