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

Authors

MUSAB T S AL-KALTAKCHI
RAID RAFI OMAR AL-NIMA
MOHAMMED A M ABDULLAH

DOI

10.3906/elk-1906-118

Abstract

The extreme learning machine (ELM) is one of the machine learning applications used for regression and classification 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 TIMIT database. The system is composed of the mel frequency cepstal coefficient (MFCC) and power normalized cepstal coefficient (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 with equal numbers of speakers of both genders with 120 speakers with eight dialects from the TIMIT database. The results demonstrate that the combination of the i-vector with the ELM for different features has the highest speaker identification accuracy (SIA) compared with the combination of the BPNN with the i-vector. The results also show that the i-vector with ELM approach is faster than the BPNN-based i-vector and it has the highest SIA.

Keywords

Speaker recognition, extreme learning machine, TIMIT database, i-vector

First Page

1236

Last Page

1245

Recommended Citation

AL-KALTAKCHI, MUSAB T S; AL-NIMA, RAID RAFI OMAR; and ABDULLAH, MOHAMMED A M (2020) "Comparisons of extreme learning machine and backpropagation-based i-vector approach for speaker identification," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 28: No. 3, Article 3. https://doi.org/10.3906/elk-1906-118
Available at: https://journals.tubitak.gov.tr/elektrik/vol28/iss3/3