  Because an impulse radio ultra-wideband (IR-UWB) radar can detect targets
with high accuracy, work through occluding materials, and operate without
contact, it is an attractive hardware solution for building silent speech
interfaces, which are non-audio-based speech communication devices. As tongue
movement is strongly engaged in pronunciation, detecting its movement is
crucial for developing silent speech interfaces. In this study, we attempted to
classify the motionless and moving states of an invisible tongue and its
related body parts using an IR-UWB radar whose antennas were pointed toward the
participant's chin. Using the proposed feature extraction algorithm and a
Gaussian mixture model - hidden Markov model, we classified two states of the
invisible tongue of four individual participants with a minimum accuracy of
90%.
