In this paper, a novel hybrid feature extraction algorithm is proposed, which implements forward masking, lateral inhibition, and temporal integration with a simple 2D psychoacoustic filter. The proposed algorithm consists of two key parts, the 2D psychoacoustic filter and cepstral mean variance normalization (CMVN). Mathematical derivation is provided to show the correctness of the 2D psychoacoustic filter based on the characteristic functions of masking effects. The effectiveness of the proposed algorithm is tested on the AURORA2 database. Extensive comparison is made against lateral inhibition (LI), forward masking (FM), CMVN, RASTA filter, the ETSI standard advanced front-end feature extraction algorithm (AFE), and the temporal warped 2D psychoacoustic filter. Experimental results show significant improvements from the proposed algorithm, a relative improvement of nearly 46.78% over the baseline mel-frequency cepstral coefficients (MFCC) system in noisy conditions.