An image transform approach for HMM based automatic lipreading

This paper concentrates on the visual front end for hidden Markov model based automatic lipreading. Two approaches for extracting features relevant to lipreading, given image sequences of the speaker's mouth region, are considered: a lip contour based feature approach which first obtains estimates of the speaker's lip contours and subsequently extracts features from them; and an image transform based approach, which obtains a compressed representation of the image pixel values that contain the speaker's mouth. Various possible features are considered in each approach, and experimental results on a number of visual-only recognition tasks are reported. It is shown that the image transform based approach results in superior lipreading performance. In addition, feature mean subtraction is demonstrated to improve the performance in multi-speaker and speaker-independent recognition tasks. Finally, the effects of video degradations to image transform based automatic lipreading are studied. It is shown that lipreading performance dramatically deteriorates below a 10 Hz field rate, and that image transform features are robust to noise and compression artifacts.