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%.

本研究基于深度神经网络，针对演讲者验证中的非线性压缩方法在光谱特征中的应用进行了探究，并提出了一种多区域设计的非线性压缩方法，通过实验证明与常用的对数方法相比，在 VoxCeleb1 和 VoxMovies 数据集上，基于功率函数的压缩方法的性能得到了很大提高，并且在 VoxMovies 数据集上，相比静态压缩方法，我们的多区域设计方法提供了更好的稳健性。