This paper describes a text-independent speaker recognition system that achieves an equal error rate of less than 1% by combining phonetic, idiolect, and acoustic features. The phonetic system is a novel language-independent speaker-recognition system based on differences among speakers in dynamic realization of phonetic features (i.e., pronunciation), rather than spectral differences in voice quality. The system exploits phonetic information from six languages to perform text-independent speaker recognition. The idiolectal system models speaker idiosyncrasies with word n-gram frequency counts computed from the output of an automatic speech recognition system. The acoustic system is a Gaussian Mixture Model-Universal Background Model that exploits the spectral differences in voice quality. All experiments were performed on the NIST 2001 Speaker Recognition Evaluation Extended Data Task.