We propose a learnable mel-frequency cepstral coefficient (MFCCs) frontend architecture for neural network based automatic speaker verification (ASV). Our architecture not only retains the simplicity and interpretability of MFCC-based features while allowing the model to be adapted to data flexibly, but also leads to performance improvement on ASV system performance.