  We propose a learnable mel-frequency cepstral coefficient (MFCC) frontend
architecture for deep neural network (DNN) based automatic speaker
verification. Our architecture retains the simplicity and interpretability of
MFCC-based features while allowing the model to be adapted to data flexibly. In
practice, we formulate data-driven versions of the four linear transforms of a
standard MFCC extractor -- windowing, discrete Fourier transform (DFT), mel
filterbank and discrete cosine transform (DCT). Results reported reach up to
6.7\% (VoxCeleb1) and 9.7\% (SITW) relative improvement in term of equal error
rate (EER) from static MFCCs, without additional tuning effort.
