1. A computer-implemented method of speaker identification in a speaker identification system, comprising: determining a representation for each first linear operator of a plurality of first linear operators, each first linear operator of the plurality of first linear operators being an approximation of a sub-matrix of a low-rank matrix T having C×F rows, of C Gaussian Mixture Model (GMM) components ×F feature dimensions, and M columns corresponding to M vectors spanning a total variability space, each sub-matrix corresponding to a c-th GMM component of the C GMM components, each first linear operator representing variability of statistical voice features with respect to a statistical model component among a plurality of statistical model components, in terms of (i) an orthogonal operator specific to the respective first linear operator of the plurality of first linear operators, (ii) a weighting operator specific to the respective first linear operator of the plurality of linear operators, wherein each respective weighting operator is a sparse matrix, and (iii) a second linear operator common to the plurality of first linear operators and having a first dimension K larger than a second dimension M of the plurality of first linear operators to maintain accuracy of the plurality of first linear operators while reducing a storage requirement for the low-rank matrix T to improve memory usage of the speaker identification system; computing statistical voice characteristics of a particular speaker using at least the representations corresponding to each of the plurality of linear operators determined; and employing the statistical voice characteristics of the particular speaker to determine whether an input speech signal corresponds to the particular speaker.