In this paper a compensation method is proposed to address the problem of limited enrolling data in speaker verification. Instead of adapting the client HMM, the technique presented here modifies the verification speech signals by maximizing the a posteriori p.d.f. in order to optimize the reduction in intra-speaker variability. The proposed approach can lead to reductions of 38.9% and 61.8% in EER and in the integral below the false-acceptation / false-rejection ROC curve, respectively.