1. An article of manufacture comprising a non-transitory computer readable storage medium having computer readable instructions tangibly embodied thereon which, when implemented, cause a computer to carry out a plurality of method steps comprising: obtaining (i) a Gaussian mixture model for expressing a distribution of one or more data features, (ii) two or more data transformations, and (iii) a set of training data related to a given speech task, wherein each of the two or more data transformations includes associated characteristics, wherein said characteristics include at least age, gender, entity affiliation, locality, demographic, and level of education; evaluating the two or more data transformations to determine which data transformation will most effectively modify the set of training data to match the model, wherein said evaluating comprises: computing a discriminative feature-space maximum mutual information (FMMI) objective function on the set of training data based on (i) the Gaussian mixture model and (ii) each of the two or more data transformations, wherein said objective function comprises a difference of log likelihood functions between a reference word sequence and a collection of multiple word sequences, and wherein each word sequence in the collection of word sequences is weighted by a language model probability; producing a first evaluation score, based on the discriminative feature-space maximum mutual information objective function, for each of the two or more data transformations; and identifying a given one of the two or more data transformations with the highest first evaluation score; evaluating, based on the Gaussian mixture model, the identified given data transformation against the set of training data related to the given speech task via the discriminative feature-space maximum mutual information (FMMI) objective function to produce a second evaluation score for the given data transformation; comparing the second evaluation score of the identified given data transformation to the first evaluation score of the identified given data transformation; and discarding the identified given data transformation if the second evaluation score is lower than the first evaluation score.