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

Musab T.S. Al-Kaltakchi, Wai L. Woo, Satnam S. Dlay, Jonathon A. Chambers

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2017 5th International Workshop on Biometrics and Forensics, IWBF 2017
PublisherIEEE
ISBN (Electronic)9781509057917
ISBN (Print)978-1-5090-5792-4
DOIs
Publication statusPublished - 29 May 2017
Externally publishedYes
Event5th International Workshop on Biometrics and Forensics, IWBF 2017 - Coventry, United Kingdom
Duration: 4 Apr 20175 Apr 2017

Conference

Conference5th International Workshop on Biometrics and Forensics, IWBF 2017
CountryUnited Kingdom
CityCoventry
Period4/04/175/04/17

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