This paper describes a novel approach for visual speech recognition. The shape of the mouth is modelled by an Active Shape Model which is derived from the statistics of a training set and used to locate, track and parameterise the speaker's lip movements. The extracted parameters representing the lip shape are modelled as continuous probability distributions and their temporal dependencies are modelled by Hidden Markov Models. We present recognition tests performed on a database of a broad variety of speakers and illumination conditions. The system achieved an accuracy of 85.42 % for a speaker independent recognition task of the first four digits using lip shape information only.

Most approaches for lip modelling are based on heuristic constraints imposed by the user. We describe the use of Active Shape Models for extracting visual speech features for use by automatic speechreading systems, where the deformation of the lip model as well as image search is based on a priori knowledge learned from a training set. We demonstrate the robustness and accuracy of the technique for locating and tracking lips on a database consisting of a broad variety of talkers and lighting conditions.

This paper describes a new approach for speaker identification based on lipreading. Visual features are extracted from image sequences of the talking face and consist of shape parameters which describe the lip boundary and intensity parameters which describe the grey-level distribution of the mouth area. Intensity information is based on principal component analysis using eigenspaces which deform with the shape model. The extracted parameters account for both, speech dependent and speaker dependent information. We built spatio-temporal speaker models based on these features, using HMMs with mixtures of Gaussians. Promising results were obtained for text dependent and text independent speaker identification tests performed on a small video database.

We describe a speechreading system that uses both, shape information from the lip contours and intensity information from the mouth area.

No abstract