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arXiv · 2110.10983

Optimizing Multi-Taper Features for Deep Speaker Verification

Xuechen Liu·Md Sahidullah·Tomi Kinnunen
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Abstract

Multi-taper estimators provide low-variance power spectrum estimates that can be used in place of the windowed discrete Fourier transform (DFT) to extract speech features such as mel-frequency cepstral coefficients (MFCCs). Even if past work has reported promising automatic speaker verification (ASV) results with Gaussian mixture model-based classifiers, the performance of multi-taper MFCCs with deep ASV systems remains an open question. Instead of a static-taper design, we propose to optimize the multi-taper estimator jointly with a deep neural network trained for ASV tasks. With a maximum improvement on the SITW corpus of 25.8% in terms of equal error rate over the static-taper, our method helps preserve a balanced level of leakage and variance, providing more robustness.

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Xuechen Liu, Md Sahidullah, Tomi Kinnunen. 2021-10-21. Optimizing Multi-Taper Features for Deep Speaker Verification. https://doi.org/10.1109/lsp.2021.3122796

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