1. A method of voice recognition, comprising the steps of: organizing a plurality of speaker data points, representing a plurality of enrollment speakers, into a data structure using high-dimensional vectors that represent characteristics of enrollment voice samples from the enrollment speakers; estimating a probability density function of a subset of the plurality of speaker data points using Parzen windows, wherein the subset comprises the approximate nearest neighbors to an unidentified voice sample from an unidentified speaker, the subset not including all speaker data points in the plurality of speaker data points, wherein the relative contributions to the density of individual speaker data points within the subset of speaker data points is based on a distance to a speaker data from the unidentified voice sample, wherein the relative contributions of speaker data points at closer distances are greater than the relative contributions of speaker data points at further distances; and identifying the unidentified speaker based on one or more speaker data points most closely matching the unidentified voice sample as indicated by the estimated probability density function.