Manual Wheelchair Automator
The speech recognition system, as one of the steering
control components of the Manual Wheelchair Automator (MWA), is designed to
benefit the end users who have lost control of their upper extremities. An
alternative joystick steering method is incorporated into the control system to
provide the users with more options.
In particular, the speech recognition
system consists of two parts: the first part is a small vocabulary training
section that constructs a model, and the second is a speech recognition section
that uses this model. This speaker independent, discrete word speech
recognition system is implemented on an 80 MHz, 32 bit micro-controller.
Much
research has been done on the existing techniques on implementing speech
recognition. The result is that a Hidden Markov Models (HMMs)-based method with
Viterbi re-estimation and Forward- Backward Model are both selected for model
construction based on its reliability and relative efficiency.
Our experimental
results indicate that Forward-Backward re- estimation provides a better
performance. Also, the speech recognition section adopts the Viterbi Algorithm
to measure the likelihood of speech input to the model of the command words.
The algorithm, design of the system, experimental data, and future improvements
are presented in detail.
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