Speechreading using probabilistic models
Computer Vision and Image Understanding - Special issue on physics-based modeling and reasoning in computer vision
Making large-scale support vector machine learning practical
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
MikeTalk: A Talking Facial Display Based on Morphing Visemes
CA '98 Proceedings of the Computer Animation
Learning-Based Approach to Real Time Tracking and Analysis of Faces
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Support Vector Regression and Classification Based Multi-View Face Detection and Recognition
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Nonlinear manifold learning for visual speech recognition
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
A Pattern Classification Approach to Dynamical Object Detection
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Invariant Face Detection with Support Vector Machines
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
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Speech recognition based on visual information is an emerging research field. We propose here a new system for the recognition of visual speech based on support vector machines which proved to be powerful classifiers in other visual tasks. We use support vector machines to recognize the mouth shape corresponding to different phones produced. To model the temporal character of the speech we employ the Viterbi decoding in a network of support vector machines. The recognition rate obtained is higher than those reported earlier when the same features were used. The proposed solution offers the advantage of an easy generalization to large vocabulary recognition tasks due to the use of viseme models, as opposed to entire word models.