1. A computerized method for speech processing using a Gaussian Mixture Model, comprising: receiving by at least one hardware processor a plurality of covariance values representing relationships between distributions of a plurality of speech coefficient values from a speech processor comprising a Gaussian Mixture Model, wherein said plurality of speech coefficient values represent a plurality of audible input speech signals recorded by at least one microphone; computing, using said at least one hardware processor, a plurality of eigenvectors and eignevalues using a principle component analysis of said plurality of covariance values; transforming, using said at least one hardware processor, said plurality of speech coefficient values using said plurality of eigenvectors and computing a plurality of second covariance values from said transformed plurality of speech coefficient values; modifying, using said at least one hardware processor, some of said plurality of second covariance values according to said plurality of eignevalues, said plurality of covariance values, and a plurality of indices of said plurality of speech coefficient values; sending said plurality of second covariance values to said speech processor comprising said Gaussian Mixture Model.