  A comparative study of the application of Gaussian Mixture Model (GMM) and
Radial Basis Function (RBF) in biometric recognition of voice has been carried
out and presented. The application of machine learning techniques to biometric
authentication and recognition problems has gained a widespread acceptance. In
this research, a GMM model was trained, using Expectation Maximization (EM)
algorithm, on a dataset containing 10 classes of vowels and the model was used
to predict the appropriate classes using a validation dataset. For experimental
validity, the model was compared to the performance of two different versions
of RBF model using the same learning and validation datasets. The results
showed very close recognition accuracy between the GMM and the standard RBF
model, but with GMM performing better than the standard RBF by less than 1% and
the two models outperformed similar models reported in literature. The DTREG
version of RBF outperformed the other two models by producing 94.8% recognition
accuracy. In terms of recognition time, the standard RBF was found to be the
fastest among the three models.
