|Waad Ben Kheder, Driss Matrouf, Moez Ajili, Jean-Francois Bonastre|
The i-vector framework witnessed great success in the past years in speaker recognition (SR). The feature extraction process is central in SR systems and many features have been developed over the years to improve the recognition performance. In this paper, we present a new feature representation which borrows a concept initially developed in computer vision to characterize textures called Local Binary Patterns (LBP). We explore the use of LBP as features for speaker recognition and show that using them as descriptors for cepstral coefficients dynamics (replacing Delta and Delta-Delta in the regular MFCC representation) results in more efficient features and yield up to 15% of relative improvement compared to the baseline system performance in both clean and noisy conditions.