We investigate the use of variable resolution spectral analysis for speaker recognition. The spectral resolution is simply determined by a unique parameter. A speaker can therefore be represented by this parameter and a stochastic model, which means that each speaker is represented in a different acoustic space. For speaker verification tasks, the likelihood ratio compared to a threshold should not depend on the representation space, so that likelihood ratios remain comparable. We experimented different spectral resolution with several classifiers but we had no improvement in the results and the classifiers turned out not to be very sensitive to the different feature sets.