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- W3130444737 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." @default.
- W3130444737 created "2021-03-01" @default.
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- W3130444737 date "2021-02-20" @default.
- W3130444737 modified "2023-10-17" @default.
- W3130444737 title "Learnable MFCCs for Speaker Verification" @default.
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- W3130444737 doi "https://doi.org/10.48550/arxiv.2102.10322" @default.
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