Abstract:
Modulation 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.
