A Temporal Network of Support Vector Machine Classifiers for the Recognition of Visual Speech

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.