Effect of the Front-End Processing on Speaker Verification Performance Using PCA and Scores Level Fusion

This paper evaluates the impact of low-level features on speaker verification performance, with an emphasis on the recently proposed MFCC variant based on asymmetric tapers (MFCC asymmetric from now on) standalone as features or followed by PCA as linear projection technique applied before the GMM-UBM back-end classifier in clean and noisy environments. The performances of the MFCC-asymmetric features are compared with: the standard Mel-Frequency Cepstral Coefficients (MFCC) that extracted from TIMIT corpus, under clean and noisy conditions. A score level fusion framework based on simples linear methods such as min, max, sum, ..., etc. and training methods like SVM is proposed to improve performance and to mitigate noise degradation. The obtained results on corrupted TIMIT database confirm the superiority of fused system in noisy environments against each system alone, and the drastic degradation of the performances of PCA based systems in the presence of environmental noise.