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arXiv · 2203.01164

Speaker recognition improvement using blind inversion of distortions

Marcos Faundez-Zanuy·Jordi Sole-Casals
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Abstract

In this paper we propose the inversion of nonlinear distortions in order to improve the recognition rates of a speaker recognizer system. We study the effect of saturations on the test signals, trying to take into account real situations where the training material has been recorded in a controlled situation but the testing signals present some mismatch with the input signal level (saturations). The experimental results shows that a combination of data fusion with and without nonlinear distortion compensation can improve the recognition rates with saturated test sentences from 80% to 88.57%, while the results with clean speech (without saturation) is 87.76% for one microphone.

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BibTeXRIS

Marcos Faundez-Zanuy, Jordi Sole-Casals. 2022-02-23. Speaker recognition improvement using blind inversion of distortions. https://arxiv.org/abs/2203.01164

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