Closed-set text-independent speaker identification system using multiple ANN classifiers

Abstract

This paper presents an Artificial Neural Network (ANN) based algorithm design to identify speakers of specific dialect using features obtained from various speaker dependent parameters of voiced speech. It is evident that speakers can be identified from their voiced sounds which have higher energy. Voice sounds are extracted from continuous speech signal from a set of trained male and female speakers. Here, feature vectors are generated from the speaker specific characteristics like pitch, linear prediction (LP) residual and empirical mode decomposition (EMD) residual of the speech. Using these feature vectors, three different ANN classifiers are designed using Multilayer Perceptron (MLP) and Recurrent Neural Network (RNN) to identify the speakers along with the dialect of the speaker. From the experiment, it is found that a hybrid classifier designed by combining all three classifiers correctly identifies more than 90% of the enrolled speakers.