Comparisons of extreme learning machine and backpropagation-based i-vector approach for speaker identification

Abstract

The extreme learning machine (ELM) is one of the machine learning applications used for regression andclassification systems. In this paper, an extended comparison between an ELM and the backpropagation neural network(BPNN)-based i-vector is given in terms of a closed-set speaker identification task using 120 speakers from the TIMITdatabase. The system is composed of the mel frequency cepstal coefficient (MFCC) and power normalized cepstalcoefficient (PNCC) approaches to form the feature extraction stage, while the cepstral mean variance normalization(CMVN) and feature warping are applied in order to mitigate the linear channel effect. The system is utilized withequal numbers of speakers of both genders with 120 speakers with eight dialects from the TIMIT database. The resultsdemonstrate that the combination of the i-vector with the ELM for different features has the highest speaker identificationaccuracy (SIA) compared with the combination of the BPNN with the i-vector. The results also show that the i-vectorwith ELM approach is faster than the BPNN-based i-vector and it has the highest SIA.