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.
|Title of host publication||Proceedings - 2017 5th International Workshop on Biometrics and Forensics, IWBF 2017|
|Publication status||Published - 29 May 2017|
|Event||5th International Workshop on Biometrics and Forensics, IWBF 2017 - Coventry, United Kingdom|
Duration: 4 Apr 2017 → 5 Apr 2017
|Conference||5th International Workshop on Biometrics and Forensics, IWBF 2017|
|Period||4/04/17 → 5/04/17|