Speaker Identification Performance Enhancement using Gaussian Mixture Model with GMM Classification Post-Processor

In this paper the application of Gaussian mixture model (GMM) classifier is investigated as an efficient post-processing method to enhance the performance of GMM-based speaker identification systems; such as Gaussian mixture model universal background model (GMM-UBM) scheme. The proposed classifier presents outstanding performance while its computational complexity is almost negligible compared to the main GMM system. Moreover, the effects of the model order of GMM classifier is studied using experimental method. Experimental results verify the superior performance of applying GMM post-processor while the proper selection of model order for this GMM has a great impact on the overall performance of the system.