Text-independent speaker recognition using neural networks
Hiroaki Hattori · 1992
A text-independent speaker recognition method using predictive neural networks is described. The speech production process is regarded as a nonlinear process, so the speaker individuality in the speech signal also includes nonlinearity. Therefore, the predictive neural network, which is a nonlinear prediction model based on multilayer perceptrons, is expected to be a more suitable model for representing speaker individuality. For text-independent speaker recognition, an ergodic model which allows transitions to any other state is adopted as the speaker model and one predictive neural network is assigned to each state. The proposed method was compared to distortion-based methods, hidden Markov model (HMM)-based methods, and a discriminative neural-network-based method through text-independent speaker recognition experiments on 24 female speakers. The proposed method gave the highest recognition accuracy of 100.0% and the effectiveness of the predictive neural networks for representing speaker individuality was clarified.>