Data-driven Gaussian Component Selection for Fast GMM-Based Speaker Verification

In this paper, a fast likelihood calculation of Gaussian mixture model (GMM) is presented, by means of dividing the acoustic space into disjoint subsets and then assigning the most relevant Gaussians to each of them. The data-driven approach is explored to select Gaussian component which guarantees that the loss, brought by pre-discarding most useless Gaussians, can be easily controlled by a manual set parameter. To avoid the rapid growth of the index table size, a two level index scheme is proposed. We adjust several set of parameters to validate our work which is expected to speed up the computation while maintaining the performance. The results of the experiments on the female part of the telephone condition of NIST SRE 2006 indicate that the speed can be improved up to 5 times over the GMM-UBM baseline system without performance loss.