Abstract: In speech recognition it is advantageous to take account of noise levels both in recognition and training. In both processes signals reaching a microphone are digitized and passed through a filter bank to be separated into frequency channels. In training, a noise estimator and a masker are used with a recognizer to prepare and store probability density functions (p.d.f.s) for each channel partially defining Markov models of words to be recognized. The p.d.f.s are derived only from input signals above noise levels but derivation is such that the whole of each p.d.f. is represented. In recognition, "distance" measurements on which recognition is based are derived for each channel. If the signal in a channel is above noise then the distance is determined, by the recognizer, from the negative logarithm of the p.d.f. but if a channel signal is below noise then the distance is determined from the negative logarithm of the cumulative distance of the p.d.f. to the noise level.