In this paper, we adopt a new factor analysis of neighborhood preserving embedding (NPE) for speaker verification under the support vector machine (SVM) framework. NPE aims at preserving the local neighborhood structure on the data and defines a low-dimensional speaker space called neighborhood preserving embedding space. We compare the proposed method with the state-of-the-art total variability approach on the telephone-telephone core condition of the NIST 2008 Speaker recognition evaluation (SRE) dataset. The experimental results indicate that the proposed NPE method outperforms the total variability approach, providing up to 24% relative improvement.

Index Terms: speaker verification, neighborhood preserving embedding, total variability, support vector machine