4. The system of claim 1 , wherein the HMM speaker recognition system is based on a single HMM per word, a single HMM per phonetic unit, and/or a single HMM per sub-phonetic unit.
1. A Hidden Markov Model (HMM) speaker recognition system, comprising: at least one data storage unit; and at least one processor programmed to: create a Gaussian Mixture Model (GMM) by pooling Gaussians from a plurality of HMM states; and normalize Gaussian weights with respect to the plurality of HMM states to provide a Text Independent (TI) speaker recognition mode in the HMM speaker recognition system; wherein the HMM speaker recognition system is selected from a group consisting of a Text Dependent (TD) HMM speaker recognition system and a Text Constrained (TC) speaker recognition system.