Front-end Channel Compensation using Mixture-dependent Feature Transformations for i-Vector Speaker Recognition

State-of-the-art session variability compensation for speaker recognition are generally based on various linear statistical models of the Gaussian Mixture Model (GMM) mean super-vectors, while front-end features are only processed by standard normalization techniques. In this study, we propose a front-end channel compensation frame-work using mixture-localized linear transforms that operate before super-vector domain modeling begins. In this approach, local linear transforms are trained for each Gaussian component of a Universal Background Model (UBM), and are applied to acoustic features according to their mixture-wise probabilistic alignment, yielding an operation that is globally non-linear. We examine Principal Component Analysis (PCA), whitening, Linear Discriminant Analysis (LDA) and Nuisance Attribute Projection (NAP) as front-end feature transformations. We also propose a method, Nuisance Attribute Elimination (NAB), which is similar to NAP but performs dimensionality reduction in addition to channel compensation. We show that the proposed frame-work can be readily integrated with a standard i-Vector system by simply applying the transformations on the first order Baum-Welch statistics and transforming the UBM. Experiments performed on the telephone trials of the NIST SRE 2010 demonstrate significant performance gain from the proposed frame-work, especially using LDA as the front-end transformation.