  In this paper we propose the inversion of nonlinear distortions in order to
improve the recognition rates of a speaker recognizer system. We study the
effect of saturations on the test signals, trying to take into account real
situations where the training material has been recorded in a controlled
situation but the testing signals present some mismatch with the input signal
level (saturations). The experimental results shows that a combination of data
fusion with and without nonlinear distortion compensation can improve the
recognition rates with saturated test sentences from 80% to 88.57%, while the
results with clean speech (without saturation) is 87.76% for one microphone.
