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Learnable MFCCs for Speaker Verification
- Publication Year :
- 2021
-
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.<br />Comment: Accepted to ISCAS 2021
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2102.10322
- Document Type :
- Working Paper