Speaker Recognition System using Gaussian Mixture Model
Kanyadara Saakshara, Kandula Pranathi, R. M. Gomathi, A. Sivasangari, P. Ajitha, T. Anandhi
Abstract
The medical field is one of the most important are as that is researched by numerous researchers in order to develop the existing system. Though there have been numerous new and innovative systems developed, there are always some challenges present in the field that needs to be solved. It is important for a healthcare system to make sure that it is secure enough and any leak of the patient data could lead to numerous other risk factors. In this paper, we propose a speaker recognition system that makes use of the Gaussian Mixture Model which identifies a person from characteristics of their voice to authenticate them to access the medical records. The model is able to determine the gender of the person to be authenticated and also recognizes the person. The model uses Cepstral Analysis as the feature extraction technique using the feature Mel Frequency Cepstral Coefficient (MFCC). The proposed work is observed to work well when compared with other existing systems and could be of much use in performing and giving access to the authorized employees in a health care system.