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Speaker identification evaluation based on the speech biometric and i-vector model using the TIMIT and NTIMIT databases

Lookup NU author(s): Musab Al-Kaltakchi, Dr Wai Lok Woo, Professor Satnam Dlay, Professor Jonathon Chambers


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© 2017 IEEE. Physiological and behavioural human characteristics are exploited in biometrics and performance metrics are used to measure some characteristic of an individual. The measure might lead to a one-to-one match, which is called authentication or one-from-N, and a match represents identification. In this paper, we exploit a speech biometric I-vector with low and fixed dimension of 100 to identify speakers. The main structure of the system consists of an I-vector with three fusion methods. It has low complexity and is efficient due to using an Extreme Learning Machine (ELM) classifier. The system is evaluated with 120 speakers from dialect regions one and four from both the TIMIT and NTIMIT databases in order to provide a fair comparison with our previous study based on the traditional Gaussian Mixture Model-Universal Background Model (GMM-UBM) with a Maximum Likelihood (ML) classifier system. The system shows identification rate improvement compared with the classical GMM-UBM.

Publication metadata

Author(s): Al-Kaltakchi MTS, Woo WL, Dlay SS, Chambers JA

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 2017 5th International Workshop on Biometrics and Forensics, IWBF 2017

Year of Conference: 2017

Online publication date: 29/05/2017

Acceptance date: 02/04/2016

Publisher: Institute of Electrical and Electronics Engineers Inc.


DOI: 10.1109/IWBF.2017.7935102

Library holdings: Search Newcastle University Library for this item

ISBN: 9781509057917