This paper introduces the use of three physiologically-motivated features for speaker identification, Residual Phase Cepstrum Coefficients (RPCC), Glottal Flow Cepstrum Coefficients (GLFCC) and Teager Phase Cepstrum Coefficients (TPCC). These features capture speaker-discriminative characteristics from different aspects of glottal source excitation patterns. The proposed physiologically-driven features give better results with lower model complexities, and also provide complementary information that can improve overall system performance even for larger amounts of data. Results on speaker identification using the YOHO corpus demonstrate that these physiologically-driven features are both more accurate than and complementary to traditional mel-frequency cepstral coefficients (MFCC). In particular, the incorporation of the proposed glottal source features offers significant overall improvement to the robustness and accuracy of speaker identification tasks.