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This paper realizes a text-independent, speaker verification system on a system on chip (SOC) platform. The system uses Mel-frequency cepstral coefficients (MFCC) features with a Gaussian mixture model-universal background model (GMM-UBM) speaker model. To deal with resource limitations, a new speaker-centric score normalization technique is introduced. This normalization technique results in a relative EER reduction of 44.9% compared to no normalization.