Nonlinear Manifold Learning for Visual Speech Recognition

A technique for representing and learning smooth nonlinear manifolds is presented and applied to sev­ eral lip reading tasks. Given a set of points drawn from a smooth manifold in an abstract feature space, the technique is capable of determining the structure of the surface and of finding the closest manifold point to a given query point. We use this technique to learn the "space of lips" in a visual speech recognition task. The learned manifold is used for tracking and extracting the lips, for interpolating between frames in an image se­ quence and for providing features for recognition. We describe a system based on Hidden Markov Models and this learned lip manifold that significantly improves the performance of acoustic speech recognizers in degraded environments. We also present preliminary results on a purely visual lip reader.