9. The feature compensation method of claim 8 , wherein the calculation of the correlation comprises: obtaining a Gaussian mixture model (GMM) probability distribution of training speech features from training speech signals that consist of two or more frames, obtaining a transition probability of a GMM mixture component between adjacent frames of the training speech features, and searching	ransition probabilities of a GMM mixture component between adjacent frames of each of the training speech signals to calculate a transition probability of the GMM mixture component that corresponds to a transition probability of a mixture component between adjacent frames of the corrupt speech features extracted from the corrupt speech signal.
10. The feature compensation method of claim 9 , wherein the generation of the compensated speech features comprises eliminating the noise features of the extracted corrupt speech features using the correlation between adjacent frames of the corrupt speech signal and the estimated noise features, wherein the correlation is based on the GMM probability distribution of the training speech features and the transition probability of a GMM mixture component.