Speaker Identification with Back Propagation Neural Network Using Tunneling Alogorithm

Abstract

speaker identification has been an active area of research in the past due to its diverse applications and it continues to be a challenging research topic. Back Propagation neural networks provides an attractive possibilities for solving signal processing & pattern classification problems. Several algorithms have been proposed for choosing the BP neural network prototypes & training the network. The selection of the BP prototypes & the network weights are the system identification problem. The proposed thesis implements an enhanced training method for BP neural network based on Tunneling algorithm. The proposed work is tested on the speaker Identification problem. Features are obtained by using linear predictive coefficients (LPC) and these features are classified by using Back propagation neural network. The efficiency of the proposed method is tested on the different speaker voices. It is shown that the use of Tunneling algorithm results in better fast learning to reach the global minima. keywords ANN, Back Propagation Training, LPCC method, MLP network.