Dynamic Features for Visual Speechreading: A Systematic Comparison

Humans use visual as well as auditory speech signals to recognize spoken words. A variety of systems have been investigated for performing thistask. The main purpose of this research was to systematically comparethe performance of a range of dynamic visual features on a speechreading task. We have found that normalization ofimages to eliminate variation due to translation, scale, and planar rotation yielded substantial improvements in generalization performanceregardless of the visual representation used. In addition, the dynamic information in the difference between successive framesyielded better performance than optical-flow based approaches, and compression by local low-pass filtering worked surprisingly betterthan global principal components analysis (PCA). These results are examined and possible explanations are explored.