A combination between VQ and covariance matrices for speaker recognition

TL;DR

This paper introduces a novel speaker recognition algorithm combining Vector Quantization and Covariance Matrix methods, enhancing accuracy and noise robustness while maintaining computational efficiency.

Contribution

It proposes a new combined VQ-CM approach that outperforms individual methods and offers a simple way to create models similar to GMMs with full covariance matrices.

Findings

Improved speaker identification rates

Enhanced robustness against noise

Comparable computational complexity

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.