Spectro-temporal smoothed auditory spectra for robust speaker identification

The performance of conventional speaker identification systems is severely compromised by interference, such as additive or convolutional noises. High-level information of the speaker provides more robust cues for identifying speakers. This paper proposes an auditory-model based spectro-temporal modulation filtering (STMF) process to capture high-level information for robust speaker identification. Text-independent closed-set speaker identification simulations are conducted on TIMIT and GRID corpora to evaluate the robustness of Auditory Cepstral Coefficients (ACCs) after the STMF process. Simulation results show ACCs´ substantial improvement over conventional MFCCs in all SNR conditions. The superior noise-suppression performance of STMF to newly developed Auditory-based Nonnegative Tensor Cepstral Coefficients (ANTCCs) is also demonstrated in low SNR conditions.