In order to describe the lip contours in a lip reading system automatically and fleetly

orthogonal compression transformation was applied to the feature extraction of lip contours. Discrete Fourier Transform (DFT) and Discrete Cosine Transform(DCT) were used to get the descriptors of lip contours in the asymmetrical lip contour model. Then the Hidden Markov Model (HMM) was trained using two kinds of descriptors as the eigenvectors of lip contours. The experiments based on isolated Chinese words show that the number of DCT descriptors needed is 15

while the number of DFT descriptors is 20 at the same recognition rate of 40%. Experiments also show that the computing quantity and the consuming time are reduced obviously by the DCT at the same recognition rate.