Biometric Human Authentication System Through Speech Using Deep Neural Networks (Dnn)

Abstract

Biometrics offers more security and convenience than traditional methods of identification. Recently, DNN has become a means of a more reliable and efficient authentication scheme. In this work, we compare two modern teaching methods: these two methods are methods based on the Gaussian mixture model (GMM) (denoted by the GMM i-vector) and methods based on deep neural networks (DNN) (denoted as the i-vector DNN). The results show that the DNN system with an i-vector is superior to the GMM system with an i-vector for various durations (from full length to 5s). DNNs have proven to be the most effective features for text-independent speaker verification in recent studies. In this paper, a new scheme is proposed that allows using DNN when checking text using hints in a simple and effective way. Experiments show that the proposed scheme reduces EER by 24.32% compared with the modern method and is evaluated for its reliability using noisy data, as well as data collected in real conditions. In addition, it is shown that the use of DNN instead of GMM for universal background modeling leads to a decrease in EER by 15.7%.

An Optimized and Privacy-Preserving System Architecture for Effective Voice Authentication over Wireless Network

“…Though, these existing conventional models and traditional algorithms have certain accuracy limitations and are less resistant to diverse phishing assaults in the modern wireless network environment. These days, owing to the development of Machine learning based techniques namely DNN (Deep Neural Network) [11], SVM (Support Vector Machine) [12], Linear Regression [13], KNN (K-Nearest Neighbours) [14], and multiple speech authentication models are built [15], [16], [17].…”