Surface Learning with Applications to Lipreading

Most connectionist research has focused on learning mappings from one space to another (eg. classification and regression). This paper introduces the more general task of learning constraint surfaces. It describes a simple but powerful architecture for learning and manipulating nonlinear surfaces from data. We demonstrate the technique on low dimensional synthetic surfaces and compare it to nearest neighbor approaches. We then show its utility in learning the space of lip images in a system for improving speech recognition by lip reading. This learned surface is used to improve the visual tracking performance during recognition. 1 Surface Learning Mappings are an appropriate representation for systems whose variables naturally decompose into “inputs” and “outputs”. To use a learned mapping, the input variables must be known and error-free and a single output value must be estimated for each input. Many tasks in vision, robotics, and control must maintain relationships between...

• Type: Conference
• Year: 1993
• Where: NIPS
• Authors: Christoph Bregler, Stephen M. Omohundro
• Keywords: Dimensional Synthetic Surfaces, Most Connectionist Research, NIPS 1993, NIPS 2007, Surface Learning Mappings