Traditional Text-Dependent Speaker Recognition (TDSR) systems model the user-specific spoken passwords with frame-based features such as MFCC and use DTW or HMM type classifiers to handle the variable length of the feature vector sequence. In this paper, we explore a direct modeling of the entire spoken password by a fixed-dimension vector called Compressed Feature Dynamics or CFD. Instead of the usual frame-by-frame feature extraction, the entire password utterance is first modeled by a 2-D Featurogram or FGRAM, which efficiently captures speaker-identity specific speech dynamics. CFDs are compressed and approximated version of the FGRAMs and their fixed dimension allows the use of simpler classifiers. Overall, the proposed FGRAM-CFD framework provides an efficient and direct model to capture the speaker-identity information well for a TDSR system. As demonstrated in trials on a 344-speaker database, compared to traditional MFCC-based

TDSR systems, the FGRAM-CFD framework shows quite encouraging performance at significantly lower complexity.