论文标题
VQ和协方差矩阵的组合，以供扬声器识别
A combination between VQ and covariance matrices for speaker recognition
论文作者
论文摘要
本文根据经典矢量量化（VQ）和协方差矩阵（CM）方法提出了一种新的算法，用于说话者识别。合并的VQ-CM方法可以通过可比的计算负担提高每种方法的识别率。它提供了一个直接的程序，可以获得与具有完整协方差矩阵的GMM相似的模型。实验结果还表明，它比单独的VQ或CM更强大。
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.
