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

Fatai Adesina

doi:10.14569/ijacsa.2010.010301

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.

Highlights

Biometrics is a measurable, physical characteristic or personal behavioral trait used to recognize the identity, or verify the claimed identity, of a candidate
A comparative study of the application of Gaussian Mixture Model (GMM) and Radial Basis Function (RBF) Neural Networks with parameters optimized with Expectation Maximization (EM) algorithm and forward and backward propagation for biometric recognition of vowels have been implemented
At the end of the study, the two models produced 80% and 81% maximum recognition rates respectively. This is better than the 80% recognition rate of the GMM proposed by Jean-Luc et al in [4] and very close to their acoustic GMM version with 83% recognition rate as well as the GMM proposed by [5]

Summary

INTRODUCTION

Biometrics is a measurable, physical characteristic or personal behavioral trait used to recognize the identity, or verify the claimed identity, of a candidate. Biometric recognition is a personal recognition system based on “who you are or what you do” as opposed to “what you know” (password) or “what you have” (ID card) [17]. The goal of voice recognition in biometrics is to verify an individual's identity based on his or her voice. Computer forensics is the application of science and engineering to the legal problem of digital evidence. It is a synthesis of science and law [8].

Voice Recognition

DATA AND TOOLS

EXPERIMENTAL APPROACH AND CRITERIA FOR PERFORMANCE EVALUATION

Findings

OF RESULTS

CONCLUSION