  In this study, we focus on nonlinear compression methods in spectral features
for speaker verification based on deep neural network. We consider different
kinds of channel-dependent (CD) nonlinear compression methods optimized in a
data-driven manner. Our methods are based on power nonlinearities and dynamic
range compression (DRC). We also propose multi-regime (MR) design on the
nonlinearities, at improving robustness. Results on VoxCeleb1 and VoxMovies
data demonstrate improvements brought by proposed compression methods over both
the commonly-used logarithm and their static counterparts, especially for ones
based on power function. While CD generalization improves performance on
VoxCeleb1, MR provides more robustness on VoxMovies, with a maximum relative
equal error rate reduction of 21.6%.
