1. A method for adapting acoustic models in a speech recognition system, the method comprising: estimating noise in a portion of a speech signal; determining a first estimated variance scaling vector using an estimated 2-order polynomial and the noise estimation, wherein the estimated 2-order polynomial represents a priori knowledge of a dependency of a variance scaling vector on noise; determining a second estimated variance scaling vector using statistics from prior portions of the speech signal; determining a variance scaling factor using the first estimated variance scaling vector and the second estimated variance scaling vector; and using the variance scaling factor to adapt an acoustic model.
1. A method for adapting acoustic models in a speech recognition system, the method comprising: estimating noise in a portion of a speech signal; determining a first estimated variance scaling vector using an estimated 2-order polynomial and the noise estimation, wherein the estimated 2-order polynomial represents a priori knowledge of a dependency of a variance scaling vector on noise; determining a second estimated variance scaling vector using statistics from prior portions of the speech signal; determining a variance scaling factor using the first estimated variance scaling vector and the second estimated variance scaling vector; and using the variance scaling factor to adapt an acoustic model. 7. The method of claim 1 , wherein the estimated 2-order polynomial is obtained by minimum square estimation of a 2-order polynomial regression on a plurality of variance scaling vectors, wherein the plurality of variance scaling vectors are estimated from speech signals collected under noisy conditions, wherein estimation of a variance scaling vector for a portion of a speech signal is performed using statistics from prior portions of the speech signal.