The performance of conventional speaker recognition systems is severely compromised by interference, such as additive or convolutional noises. High-level information of the speaker is considered more robust cues for recognizing speakers. This paper proposes an auditory-model based spectral features, auditory cepstral coefficients (ACCs), and a spectro-temporal modulation filtering (STMF) process to capture high-level information for robust speaker recognition. Text-independent closed-set speaker recognition experiments are conducted on TIMIT and GRID corpora to evaluate the robustness of ACCs and benefits of the STMF process. Experimental results show ACCs’ significant improvement over conventional MFCCs in all SNR conditions. The superior performance of STMF to newly developed ANTCCs is also demonstrated in low SNR conditions.