Towards speaker identification system based on dynamic neural network

Abstract

The conventional, Finite Impulse Response and Lattice-Ladder multilayer perceptron (MLP) structures with 4, 8 and 16 hidden neurons were verified for speaker identification. The experiments were performed on 10 speakers, 3 Lithuanian words, 7 sessions’ database. Identification performance was compared against two baseline methods: Vector Quantization (Linde-Buzo-Gray) and Gauss Mixture Models (Expectation Maximization). Increase of neuron number in hidden layer has led to smaller mean square errors on training dataset. A Finite Impulse Response MLP showed smaller mean square errors values. The results of experimental investigation show that neural networks can be used for speaker identification system as they outperform baseline methods. The best identification rate was archived by a multilayer perceptron with 4 hidden neurons and Finite Impulse Response MLP with 8 hidden neurons.