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This paper describes the Nuance–Politecnico di Torino (NPT) speaker recognition system submitted to the NIST SRE16 evaluation campaign. Included are the results of post-evaluation tests, focusing on the analysis of the performance of generative and discriminative classifiers, and of score normalization. The submitted system combines the results of four GMM-IVector models, two DNN-IVector models and a GMM-SVM acoustic system. Each system exploits acoustic front-end parameters that differ by feature type and dimension. We analyze the main components of our submission, which contributed to obtaining 8.1% EER and 0.532 actual Cprimary in the challenging SRE16 Fixed condition.