Factor Analysis of Neighborhood-Preserving Embedding for Speaker Verification

Abstract

SUMMARYIn this letter, we adopt a new factor analysis of neighborhood-preserving embedding (NPE) for speaker verification. 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.

Locality Preserving Discriminant Projection for Speaker Verification

“…As an application of probabilistic principal component analysis (PPCA), total variability factor analysis only analyzes the speech data from a global perspective [9] [10]. To compensate for the deficiency, we introduced locality preserving projection (LPP) [11], neighborhood preserving embedding (NPE) [12], and discriminant neighborhood embedding (DNE) [13] to speaker verification. By constructing a graph containing the neighborhood information of the speech data, the inherent local neighborhood relationship of the speech data is optimally preserved.…”