Adaptation Parameters Effects in GMM Post-Processor for Structural Gaussian Mixture Model Based Text-Independent Speaker Verification

In this paper, the effects of adaptation parameters selection on the performance of a postprocessing Gaussian mixture model (GMM) called GMM identifier, used in GMM based speaker verification system are studied. Experimental results show the importance of proper parameters choice in the adaptation of the post-processor GMM model. Models implemented, trained, and tested using a Farsi speech dataset with 90 speakers. Combinations of priors, means, and covariance adaptation were examined and multiple orders of GMM identifier from 4 to 128 were evaluated.