Gaussian mixture models (GMMs) and ergodic hidden Markov models (HMMs) have been successfully applied to model short-term acoustic vectors for speaker recognition systems. Prosodic features are known to carry information concerning the speaker's identity and they can be combined with the short-term acoustic vectors in order to increase the performance of the speaker recognition system. In this paper, a statistical approach using pitch-dependent GMMs for modeling speakers is presented. This new approach is capable of simultaneously modeling the statistical distributions of the short-term acoustic vectors and long-term prosodic features