Abstract
This thesis discusses the application of predictive text generation to
enhance the communication abilities of physically disabled persons.
Predictive techniques exploit the statistical redundancies of language
to accelerate and amplify user inputs, thereby increasing communication
efficiency. Acceleration is achieved by making more likely language
elements faster to select, while amplification is accomplished by
selection of concatenated elements. Novel adaptive language models are
used to enhance versatility and flexibility.
Predictive text generation (PTG) is defined, existing PTG systems are
surveyed and a framework is developed for classifying and evaluating
them. Simulation studies and a user pilot experiment are persented for a
particularly significant PTG system called \fIPredict\fR. Experience
with Predict led to the Reactive Keyboard concept of PTG. The
research results presented highlight design issues common to the two
systems.
The Reactive Keyboard concept is introduced along with its prediction
technique, data structure and two user interface implementations:
\fIRK-button\fR and RK-pointer. A clear distinction is made between
the system's user interface and the underlying model it employs. A
variable-length n-gram language model is presented which adaptively
gathers statistics from the user's text input. A number of alternative
model structures are discussed and details of a novel, highly compact,
data storage technique are given. Context conditioned candidate strings,
which are predicted by the model, are ordered according to popularity and
displayed for selection on a VDU.
Notes
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