This paper presents an overview of the architecture and algorithms implemented in IBM's text-independent speaker verification system developed for the 2002 NIST Speaker Recognition Evaluation, particularly for the 1-speaker detection task using cellular test data. We describe individual components including a Gaussianization front-end, celluar-codec post-processing, modeling, discriminative optimization and scoring steps. A combination of multiple, data-perturbed systems using a discriminative objective so as to achieve optimum performance for a low false alarm operating region obtained the top performance in the N E T 2002 1-speaker detection task.
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