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. Download(.doc)
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