Multiple linear transforms
Nagendra Kumar Goel, R. Gopinath · 2002
Heteroscedastic discriminant analysis (HDA) has been proposed as a replacement for linear discriminant analysis (LDA) in speech recognition systems that use mixtures of diagonal covariance Gaussians to model the data. Typically HDA and LDA involve a dimension reduction of the feature space. A specific version HDA that involves no dimension reduction; and is popularly known as maximum likelihood linear transform (MLLT) is often used on the feature space to give significant improvements in performance. MLLT approximately diagonalizes the class covariances, and in effect, tries to approximate the performance of a full-covariance-system. However, the performance of a full-covariance system could in some cases be much better than using MLLT-based diagonal covariance system. We propose the method of multiple linear transforms, that bridges this gap in performance, while maintaining the speed efficiency of a diagonal covariance system. This technique improves the performance of a diagonal covariance system, over what could be obtained from HDA or MLLT.