title: Efficient invariant features for sensor variability compensation in speaker recognition.
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.
authors.affiliation: Research Laboratory in Electrical Engineering and Automatic LREA, University of MEDEA, Ain D'heb, Medea 26000, Algeria. alimohad@msn.com.
authors.forename: Abdennour
authors.initials: A
authors.lastname: Alimohad
authors.affiliation: School of Computing, Engineering and Information Sciences, Northumbria University, Newcastle Upon Tyne NE2 1XE, UK. Ahmed.Bouridane@northumbria.ac.uk.
authors.forename: Ahmed
authors.initials: A
authors.lastname: Bouridane
authors.affiliation: Department of Electronics Engineering, University of Blida, Blida BP 270, Algeria. abderguessoum@yahoo.com.
authors.forename: Abderrezak
authors.initials: A
authors.lastname: Guessoum
mesh_terms: D000465:Algorithms; D006801:Humans; D008390:Markov Chains; D008954:Models, Biological; D012637:Security Measures; D013060:Speech; D049250:Speech Recognition Software