This paper presents an analysis of the cepstral-time matrix. The coefficients of the cepstral-time matrix are found to be similar to the standard cepstral vector with differential features augmented on. It is also shown that the cepstral-time matrix is inherently robust to convolutional channel distortion. Spectral-subtraction, Wiener filtering and model combination are extended into two-dimensions where improved noise robustness is achieved. Experimental results using the NOISEX database with noise and channel distorted speech are presented.,