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Lookup NU author(s): Emeritus Professor Satnam Dlay
This is the authors' accepted manuscript of an article that has been published in its final definitive form by Birkhauser, 2021.
For re-use rights please refer to the publisher's terms and conditions.
© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.In this article, a novel combined i-vector and an Extreme Learning Machine (ELM) is proposed for speaker identification. The ELM is chosen because it is fast to train and has a universal approximator property. Four combinations of features based on Mel Frequency Cepstral Coefficient and Power Normalized Cepstral Coefficient are used. Besides, seven fusion methods are exploited. The system is evaluated with three different databases, namely: the SITW 2006, NIST 2008, and the TIMIT database. This work employs the 2016 SITW database for the first time for speaker identification using the integration between the ELM and i-vector approach. From each database, 120 speakers with 1200 speech utterances are used (overall 360 speakers with 3600 speech utterances). Furthermore, comprehensive evaluations are exploited with a wide range of realistic background noise types (Stationary noise AWGN and Non-Stationary Noise types) with the handset effect. The proposed system is compared with the Gaussian Mixture Model-Universal Background Model (GMM-UBM) and other states of the art approaches. The results show that the i-vector method outperforms the GMM-UBM approach and other state- of-the-art methods under specific conditions, and that fusion techniques can be used to improve robustness to noise and handset effects.
Author(s): Al-Kaltakchi MTS, Abdullah MAM, Woo WL, Dlay SS
Publication type: Article
Publication status: Published
Journal: Circuits, Systems, and Signal Processing
Year: 2021
Volume: 40
Pages: 4903-4923
Print publication date: 01/10/2021
Online publication date: 25/03/2021
Acceptance date: 08/03/2021
Date deposited: 19/05/2021
ISSN (print): 0278-081X
ISSN (electronic): 1531-5878
Publisher: Birkhauser
URL: https://doi.org/10.1007/s00034-021-01697-7
DOI: 10.1007/s00034-021-01697-7
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