TongueBoard
Richard J. Li, Jason Wu, Thad E. Starner · 2019
We present TongueBoard, a retainer form-factor device for recognizing non-vocalized speech. TongueBoard enables absolute position tracking of the tongue by placing capacitive touch sensors on the roof of the mouth. We collect a dataset of 21 common words from four user study participants (two native American English speakers and two non-native speakers with severe hearing loss). We train a classifier that is able to recognize the words with 91.01% accuracy for the native speakers and 77.76% accuracy for the non-native speakers in a user dependent, offline setting. The native English speakers then participate in a user study involving operating a calculator application with 15 non-vocalized words and two tongue gestures at a desktop and with a mobile phone while walking. TongueBoard consistently maintains an information transfer rate of 3.78 bits per decision (number of choices = 17, accuracy = 97.1%) and 2.18 bits per second across stationary and mobile contexts, which is comparable to our control conditions of mouse (desktop) and touchpad (mobile) input.