A quadratic discriminant classification technique was used to classify spectral measurements from vowels spoken by men, women, and children. The parameters used to train the discriminant classifier consisted of various combinations of fundamental frequency and the three lowest formant frequencies. Several nonlinear auditory transforms were evaluated. Unlike previous studies using a linear discriminant classifier, there was no advantage in category separability for any of the nonlinear auditory transforms over a linear frequency scale, and no advantage for spectral distances over absolute frequencies. However, it was found that parameter sets using nonlinear transforms and spectral differences reduced the differences between phonetically equivalent tokens produced by different groups of talkers.