Voice Classification Based on Fast Independent Component Analysis to Support Nuclear Power Plant Security

Abstract

National vital objects have important role in national development,so they require special protection. Nuclear power plant (NPP) is one of them. Access restriction is required to prevent the NPP from potential hazards. The restriction can be improved by using face, fingerprints, retina, iris, and voice password. The improvement will enhance the security of the NPP. This research has implemented pattern recognition and classification of voice passwords. The passwords were a, i, u, e, and o. The features vector was searched by using Fast ICA method while the pattern classification was performed by minimum Euclidean method. The purpose of this research is to recognize and classify those letter password, so people who have access to the nuclear area can be distinguished. The methodology of this research consists of input data, pre-processing data, feature extraction, and classification. Pre-processing was done by normalization, denoising, centering, and whitening. Feature extraction was performed by Fast ICA method, and classification was done by minimum Euclidean distance. The results show that Fast ICA and minimum Euclidean methods can 100 % distinguish between the employees who have access permit and those who have no access permit. When an employee with access permit says aiueo”, it will be recognized as password, whereas when an employee with no access permit says the password, it will be recognized not as the password.