1. A method of text independent speaker recognition, comprising: extracting feature vectors from initial frames of speech generated responsive to text-independent speech from a user; clustering the extracted feature vectors to generate a plurality of clusters; modelling each of the plurality of clusters as a Gaussian Mixture Model that collectively form a speaker profile for the user; setting a different-state transition probability and a same-state transition probability for each of the Gaussian Mixture Models, the same-state transition probability having a much greater value than the different-state transition probability; capturing additional frames of speech from additional text-independent speech from a speaker; extracting feature vectors from the additional frames; and determining likelihoods that each of the additional frames belongs to each of the Gaussian Mixture Models based on the same-state and different-state transition probabilities, wherein the determining likelihoods includes, for each additional frame of speech, determining a log likelihood (loglk) variable for each cluster to determine the probability that the additional frame of speech belongs to that particular cluster; and determining whether the speaker is an authorized user from the determined likelihoods.
2. The method of claim 1 wherein setting a different-state transition probability and a same-state transition probability for each of the Gaussian Mixture Models comprises setting the different-state transition probability between Gaussian Mixture Models to a value of 0.05 and setting the same-state transition probability for each Gaussian Mixture Model to a value of 0.95.