Learnable MFCCs for Speaker Verification
Sound
2021-02-23 v1 Machine Learning
Audio and Speech Processing
Abstract
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.
Keywords
Cite
@article{arxiv.2102.10322,
title = {Learnable MFCCs for Speaker Verification},
author = {Xuechen Liu and Md Sahidullah and Tomi Kinnunen},
journal= {arXiv preprint arXiv:2102.10322},
year = {2021}
}
Comments
Accepted to ISCAS 2021