A comparative study of several parameterizations for speaker recognition
Sound
2022-03-02 v1 Machine Learning
Audio and Speech Processing
Abstract
This paper presents an exhaustive study about the robustness of several parameterizations, in speaker verification and identification tasks. We have studied several mismatch conditions: different recording sessions, microphones, and different languages (it has been obtained from a bilingual set of speakers). This study reveals that the combination of several parameterizations can improve the robustness in all the scenarios for both tasks, identification and verification. In addition, two different methods have been evaluated: vector quantization, and covariance matrices with an arithmetic-harmonic sphericity measure.
Cite
@article{arxiv.2203.00513,
title = {A comparative study of several parameterizations for speaker recognition},
author = {Marcos Faundez-Zanuy},
journal= {arXiv preprint arXiv:2203.00513},
year = {2022}
}
Comments
4 pages