  Several speaker identification systems are giving good performance with clean
speech but are affected by the degradations introduced by noisy audio
conditions. To deal with this problem, we investigate the use of complementary
information at different levels for computing a combined match score for the
unknown speaker. In this work, we observe the effect of two supervised machine
learning approaches including support vectors machines (SVM) and na\"ive bayes
(NB). We define two feature vector sets based on mel frequency cepstral
coefficients (MFCC) and relative spectral perceptual linear predictive
coefficients (RASTA-PLP). Each feature is modeled using the Gaussian Mixture
Model (GMM). Several ways of combining these information sources give
significant improvements in a text-independent speaker identification task
using a very large telephone degraded NTIMIT database.
