1. A computer-implemented method of transforming and combining a plurality of models representing training speakers into a model for a test speaker, comprising: receiving adaptation data from the test speaker; utilizing a computer processor that is a functional component of the computer to select a set of cohort speakers from the training speakers; transforming a plurality of models representing the cohort speakers; and combining the plurality of transformed models to form the model for the test speaker; wherein selecting a set of cohort speakers comprises: generating a Gaussian Mixture Model (GMM) for each of the training speakers in the plurality of training speakers, wherein generating the GMM for each of the training speakers comprises calculating a probability mixture component for each GMM, wherein the Gaussian Mixture Models are calculated according to the following equation: b ⁢ ⁢ ( O ) = ∑ k = 1 M ⁢ ⁢ c k ⁢ G ⁢ ⁢ ( O , μ k , U k ) ; where b(O) is an output probability of observation sequence O, c k is a weight for k-th mixture component, G is a Gaussian function with mean vector μ k and convariance matrix U k , and wherein the probability mixture component is calculated according to the following equation: p ⁢ ⁢ ( k | o ⁢ ⁢ ( t ) , Λ n ) = c k ⁢ G ⁢ ⁢ ( o ⁢ ⁢ ( t ) , μ k , σ k 2 ) ∑ i = 1 M ⁢ ⁢ c i ⁢ G ⁢ ⁢ ( o ⁢ ⁢ ( t ) , μ i , σ i 2 ) ; where p(k|O(t),Λ n ) is a posterior probability for mixture component k and weight vector Λ n , c k is a weight for k-th mixture component, and G is a Gaussian function with observation vector O(t) at time t, mean vector μ k and diagonal covariance matrix σ 2 i ; determining a similarity between the models for the training speakers and the adaptation data from the test speaker based at least in part on the Gaussian Mixture Models and the probability mixture components; and selecting as a set of cohort speakers, training speakers that have models having a desired similarity to the adaptation data from the test speaker; wherein transforming the plurality of models representing the cohort speakers comprises: receiving model data for each of the models representing the cohort speakers; and adapting the model data for each of the models representing the cohort speakers based on the adaptation data from the test speaker, wherein the model data for each of the models representing the cohort speakers is adapted independently from one another; and wherein combining the plurality of transformed models comprises: determining a weight vector for each of the transformed models, the weight vector for each of the transformed models being based at least partially on the adaptation data from the test speaker; and combining the plurality of transformed models based on the weight vectors to form the model for the test speaker.