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

TL;DR

This study compares Gaussian Mixture Models and Radial Basis Function networks for voice recognition, showing that GMM slightly outperforms standard RBF, while DTREG RBF achieves the highest accuracy, and standard RBF is the fastest.

Contribution

It provides a comparative analysis of GMM and RBF models for voice recognition, highlighting their accuracy and speed differences on the same dataset.

Findings

GMM outperforms standard RBF by less than 1% in accuracy.

DTREG RBF achieves 94.8% recognition accuracy.

Standard RBF is the fastest model.

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.