Speaker recognition improvement using blind inversion of distortions
Sound
2022-03-03 v1 Machine Learning
Audio and Speech Processing
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.
Cite
@article{arxiv.2203.01164,
title = {Speaker recognition improvement using blind inversion of distortions},
author = {Marcos Faundez-Zanuy and Jordi Sole-Casals},
journal= {arXiv preprint arXiv:2203.01164},
year = {2022}
}
Comments
4 pages