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Sensors 2014, 14(10), 19007-19022; doi:10.3390/s141019007

Efficient Invariant Features for Sensor Variability Compensation in Speaker Recognition

1
Research Laboratory in Electrical Engineering and Automatic LREA, University of MEDEA, Ain D'heb, Medea 26000, Algeria
2
School of Computing, Engineering and Information Sciences, Northumbria University, Newcastle Upon Tyne NE2 1XE, UK
3
Department of Electronics Engineering, University of Blida, Blida BP 270, Algeria
*
Author to whom correspondence should be addressed.
Received: 1 May 2014 / Revised: 1 August 2014 / Accepted: 26 September 2014 / Published: 13 October 2014
(This article belongs to the Section Physical Sensors)
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Abstract

In this paper, we investigate the use of invariant features for speaker recognition. Owing to their characteristics, these features are introduced to cope with the difficult and challenging problem of sensor variability and the source of performance degradation inherent in speaker recognition systems. Our experiments show: (1) the effectiveness of these features in match cases; (2) the benefit of combining these features with the mel frequency cepstral coefficients to exploit their discrimination power under uncontrolled conditions (mismatch cases). Consequently, the proposed invariant features result in a performance improvement as demonstrated by a reduction in the equal error rate and the minimum decision cost function compared to the GMM-UBM speaker recognition systems based on MFCC features. View Full-Text
Keywords: speaker recognition; invariant features; MFCCs; GMM-UBM; sensor variability; DET curve speaker recognition; invariant features; MFCCs; GMM-UBM; sensor variability; DET curve
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Alimohad, A.; Bouridane, A.; Guessoum, A. Efficient Invariant Features for Sensor Variability Compensation in Speaker Recognition. Sensors 2014, 14, 19007-19022.

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