8. An apparatus for enhancing speech recognition in noisy environments, said apparatus comprising: an arrangement for 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; an arrangement for 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.
8. An apparatus for enhancing speech recognition in noisy environments, said apparatus comprising: an arrangement for 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; an arrangement for 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. 9. The apparatus according to claim 8 , wherein the adaptation algorithm allows an adaptation of a mean and a variance of the atoms and does not constrain atoms of a same band to undergo a same transform.