A combination between VQ and covariance matrices for speaker recognition
Sound
2022-03-24 v1 Cryptography and Security
Audio and Speech Processing
Abstract
This paper presents a new algorithm for speaker recognition based on the combination between the classical Vector Quantization (VQ) and Covariance Matrix (CM) methods. The combined VQ-CM method improves the identification rates of each method alone, with comparable computational burden. It offers a straightforward procedure to obtain a model similar to GMM with full covariance matrices. Experimental results also show that it is more robust against noise than VQ or CM alone.
Cite
@article{arxiv.2203.12306,
title = {A combination between VQ and covariance matrices for speaker recognition},
author = {Marcos Faundez-Zanuy},
journal= {arXiv preprint arXiv:2203.12306},
year = {2022}
}
Comments
5 pages, published in 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221), Salt Lake City, UT, USA