  Mel-scale spectrum features are used in various recognition and
classification tasks on speech signals. There is no reason to expect that these
features are optimal for all different tasks, including speaker verification
(SV). This paper describes a learnable front-end feature extraction model. The
model comprises a group of filters to transform the Fourier spectrum. Model
parameters that define these filters are trained end-to-end and optimized
specifically for the task of speaker verification. Compared to the standard
Mel-scale filter-bank, the filters' bandwidths and center frequencies are
adjustable. Experimental results show that applying the learnable acoustic
front-end improves speaker verification performance over conventional Mel-scale
spectrum features. Analysis on the learned filter parameters suggests that
narrow-band information benefits the SV system performance. The proposed model
achieves a good balance between performance and computation cost. In
resource-constrained computation settings, the model significantly outperforms
CNN-based learnable front-ends. The generalization ability of the proposed
model is also demonstrated on different embedding extraction models and
datasets.
