Covariance-enhanced discriminant analysis.

Supplementary material to: Covariance-Enhanced Discriminant Analysis

Between-Class Covariance Correction For Linear Discriminant Analysis in Language Recognition

Linear Discriminant Analysis (LDA) is one of the most widely-used channel compensation techniques in current speaker and language recognition systems. In this study, we propose a technique of Between-Class Covariance Correction (BCC) to improve language recognition performance. This approach builds on the idea of WithinClass Covariance Correction (WCC), which was introduced as a means to compen...

Duration dependent covariance regularization in PLDA modeling for speaker verification

In this paper, we present a covariance regularized probabilistic linear discriminant analysis (CR-PLDA) model for text independent speaker verification. In the conventional simplified PLDA modeling, the covariance matrix used to capture the residual energies is globally shared for all i-vectors. However, we believe that the point estimated i-vectors from longer speech utterances may be more acc...

Microsoft Word - Model based Mixture Discriminant Analysis-An Exprimental –

The subject of this paper is an experimental study of a discriminant analysis (DA) based on Gaussian mixture estimation of the class-conditional densities. Five parameterizations of the covariance matrixes of the Gaussian components are studied. Recommendation for selection of the suitable parameterization of the covariance matrixes is given.

Covariance Matrix Estimation in Time Series

Covariances play a fundamental role in the theory of time series and they are critical quantities that are needed in both spectral and time domain analysis. Estimation of covariance matrices is needed in the construction of confidence regions for unknown parameters, hypothesis testing, principal component analysis, prediction, discriminant analysis among others. In this paper we consider both l...