Optimizing Multi-Taper Features for Deep Speaker Verification

• URL: http://arxiv.org/abs/2110.10983v1
• Date: Thu, 21 Oct 2021 08:56:11 GMT
• Title: Optimizing Multi-Taper Features for Deep Speaker Verification
• Authors: Xuechen Liu, Md Sahidullah, Tomi Kinnunen
• Abstract summary: 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.
• Score: 21.237143465298505
• License: http://creativecommons.org/licenses/by/4.0/
• 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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