Efficient Invariant Features for Sensor Variability Compensation in Speaker Recognition

Abdennour Alimohad, Ahmed Bouridane, Abderrezak Guessoum

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
10 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)19007-19022
Issue number10
Publication statusPublished - Oct 2014


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