1. A method of enhancing speech recognition in noisy environments, said method comprising the steps of: providing at least one initial Compound Gaussian Mixture model comprising a formally equivalent model structured into parameters comprising atoms, wherein Gaussian components are sliced into Gaussian components of smaller dimension termed bands, said bands being clustered into atoms; applying an adaptation algorithm to compute an adapted set of parameters to be utilized in an adapted Compound Gaussian Mixture model using enrollment data of a speaker; wherein said adaptation algorithm comprises: computing an adapted set of parameters given enrollment data of a speaker via linear combination of (1) an initial set of parameters and (2) an estimated set of parameters, said estimated set estimated via calculating a maximum likelihood estimate according to a maximum likelihood criterion on the enrollment data; wherein the adaptation algorithm involves reducing mismatch between characteristics of speech data that are specific to a speaker in an acoustic environment and characteristics of the initial Compound Gaussian Mixture model trained on general speech data; and wherein a processor is used to construct the adapted Compound Gaussian Mixture model to enhance speech recognition in the noisy environment.