Most of current state-of-the-art speaker verification (SV) systems use Gaussian mixture model (GMM) to represent the universal background model (UBM) and the speaker models (SM). For an SV system that employs log-likelihood ratio between SM and UBM to make the decision, its computational efficiency is largely determined by the GMM computation. This paper attempts to speedup GMM computation by converting a continuous-density GMM to a single or a mixture of discrete densities using scalar quantization. We investigated a spectrum of such discrete models: from high-density discrete models to discrete mixture models, and their combination called high-density discrete-mixture models. For the NIST 2002 SV task, we obtained an overall speedup by a factor of 2–100 with little loss in EER performance.