3. A method for minimizing the effect of an unknown signal bias on an input speech signal during the testing phase of a speech recognition system, comprising: (a) computing an estimate of the bias for each utterance of the speech signal based on maximizing a likelihood function by initially utilizing a set of centroids generated by a training model; (b) subtracting the estimate of the bias from the input speech signal to obtain a tentative speech value; (c) repeating steps (a) and (b) a predetermined number of times, wherein each subsequent computed estimate of the bias is based on the previous tentative speech value, resulting in a reduced bias speech signal value; and (d) utilizing the reduced bias speech signal value as input to a speech recognizer.
3. A method for minimizing the effect of an unknown signal bias on an input speech signal during the testing phase of a speech recognition system, comprising: (a) computing an estimate of the bias for each utterance of the speech signal based on maximizing a likelihood function by initially utilizing a set of centroids generated by a training model; (b) subtracting the estimate of the bias from the input speech signal to obtain a tentative speech value; (c) repeating steps (a) and (b) a predetermined number of times, wherein each subsequent computed estimate of the bias is based on the previous tentative speech value, resulting in a reduced bias speech signal value; and (d) utilizing the reduced bias speech signal value as input to a speech recognizer. 4. The method of claim 3, wherein a vector quantization method is utilized to generate the centroids of step (a).