In the present paper, we investigate the use of "Rasta-type cepstral processing techniques", for speech recognition under mismatched speaking rate conditions. The acoustic models are trained on an isolated-word speech data base and and then tested on a continuous speech data base. The speaking rates in the two data bases are significantly different. Using high resolution phoneme-context dependent models, the high-pass cepstrum is shown to perform comparable for matched conditions and outperforms the other techniques.