Learnable MFCCs for Speaker Verification
CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；LORIA（洛里亚实验室）；Université de Lorraine（洛林大学）；School of Computing（计算机学院）；University of Eastern Finland（东芬兰大学）
英文摘要：
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.
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