3. The speech recognition method of claim 1 , wherein the linear regression type distribution is a PCMLLR type distribution of the form: 
 p c ( y|m )=| A c (r m ) |N ( A c (r m ) y+b c (r m ) ;μ x (m) ,Σ x (m) ) Where A c (r m ) and b c (r m ) are the PCMLLR transforms, y is the feature vector for noisy speech, m is a component index indicating a probability distribution relating a word or part thereof to a feature vector, μ x (m) is the mean of the distribution m in the second noise environment and Σ x (m) the covariance matrix of the distribution m in the second noise environment.
3. The speech recognition method of claim 1 , wherein the linear regression type distribution is a PCMLLR type distribution of the form: 
 p c ( y|m )=| A c (r m ) |N ( A c (r m ) y+b c (r m ) ;μ x (m) ,Σ x (m) ) Where A c (r m ) and b c (r m ) are the PCMLLR transforms, y is the feature vector for noisy speech, m is a component index indicating a probability distribution relating a word or part thereof to a feature vector, μ x (m) is the mean of the distribution m in the second noise environment and Σ x (m) the covariance matrix of the distribution m in the second noise environment. 7. The speech recognition method of claim 3 , wherein estimating the mimicked transform parameters comprises approximating the variance with an averaged variance.