ABSTRACT
Neural networks, with their remarkable ability
to derive meaning from complicated or imprecise data, can be used to extract
patterns and detect trends that are too complex to be noticed by either humans
or other computer techniques. A trained neural network can be thought of as an
expert in the category of information it has been given to analyze.
Neural network is applied in LIP READING,
one of the easiest ways to recognize the speech. It is one of the latest
techniques widely preferred for speech recognition. We describe a lip reading system
that uses both, shape information from the lip contours and intensity information
from the mouth area. Shape information
is obtained by
tracking and parameterising the
inner and outer
lip boundary in
an image sequence. Intensity information is extracted from a
grey level model, based on principal component analysis. In comparison to other
approaches, the intensity area deforms
with the shape model to ensure that similar object features are represented
after non-rigid deformation of the lips.
We describe speaker independent recognition experiments based on these
features. Preliminary results suggest that similar performance can be achieved
by using either shape or intensity information and slightly higher performance
by their combined use.
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