Learnable Nonlinear Compression for Robust Speaker Verification
Abstract
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%.
Cite
@article{arxiv.2202.05236,
title = {Learnable Nonlinear Compression for Robust Speaker Verification},
author = {Xuechen Liu and Md Sahidullah and Tomi Kinnunen},
journal= {arXiv preprint arXiv:2202.05236},
year = {2022}
}
Comments
Accepted by ICASSP2022