Multiple linear transforms

In the past several years, Linear Discriminant Analysis (LDA) is being replaced by Heteroscedastic Discriminant Analysis (HDA), to improve the performance of a recognition system that uses a mixture of diagonal covariance prototypes to model the data. A specific version HDA, popularly known as Maximum Likelihood Linear Transform (MLLT) is also used, on the features finally obtained. However the performance of such systems is not as good as could be obtained for a corresponding system that uses full covariance matrices. We propose the method of Multiple Linear Transforms (MLT), that bridges this gap in performance, while maintaining the speed efficiency of a diagonal covariance system. In other words, this technique improves the performance of a diagonal covariance system, over what could be obtained from HDA, or MLLT.