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We present a method to boost the performance of probabilistic generative models that work with i-vector representations. The proposed approach deals with the non- Gaussian behavior of i-vectors by performing a simple length normalization. This nonlinear transformation allows the use of probabilistic models with Gaussian assumptions that yield equivalent performance to that of more complicated systems based on Heavy-Tailed assumptions. Significant performance improvements are demonstrated on the telephone portion of NIST SRE 2010.