This thesis investigates the feasibility of using both vocal source and vocal tract information
to improve speaker recognition performance. Conventional speaker recognition systems
typically employ vocal tract related acoustic features, eg the Mel-frequency cepstral
coefficients (MFCC), for discriminative purpose. Motivated by the physiological significance
of the vocal source and vocal tract system in speech production, this thesis develops a
speaker recognition system to effectively incorporate these two complementary information …