  Speaker identification is a powerful, non-invasive and in-expensive biometric
technique. The recognition accuracy, however, deteriorates when noise levels
affect a specific band of frequency. In this paper, we present a sub-band based
speaker identification that intends to improve the live testing performance.
Each frequency sub-band is processed and classified independently. We also
compare the linear and non-linear merging techniques for the sub-bands
recognizer. Support vector machines and Gaussian Mixture models are the
non-linear merging techniques that are investigated. Results showed that the
sub-band based method used with linear merging techniques enormously improved
the performance of the speaker identification over the performance of wide-band
recognizers when tested live. A live testing improvement of 9.78% was achieved
