Comparative Study of Gaussian Mixture Model and Radial Basis Function for Voice Recognition

Abstract

Abstract-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.

Machine Learning for Haptics: Inferring Multi-Contact Stimulation From Sparse Sensor Configuration

“…Since the task is not a density estimation, GMM cannot be applied directly and the data would need to be transformed. For fitting the RBFs a good initialization is needed (Anifowose, 2012) in order to obtain consistently good results. The nearest neighbor approach k-Max we propose shows superior performance in our setting.…”