1. A computerized method for clustering at least one of speech segments and speech signals of a speaker using a Gaussian Mixture Model, comprising: receiving a plurality of audible input speech signals originated from said speaker and recorded by at least one microphone; using at least one hardware processor to execute a code for clustering the at least one of speech segments and speech signals of said said speaker with a reduced error rate: computing a plurality of eigenvectors and eignevalues using a principle component analysis of a plurality of covariance values representing relationships between distributions of a plurality of speech coefficient values of said plurality of audible input speech signals, transforming 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 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; and estimating an identity of said speaker by processing said plurality of second covariance values using a Gaussian Mixture Model; using said identity to cluster the at least one of speech segments and speech signals of said speaker with the reduced error rate.