TongueBoard: An Oral Interface for Subtle Input

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

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Richard Li, Jason Wu, and Thad Starner. 2019. TongueBoard: An Oral Interface for Subtle Input. In Proceedings of the 10th Augmented Human International Conference 2019 (AH2019). ACM, New York, NY, USA, Article 1, 9 pages. DOI: https://doi.org/10.1145/3311823.3311831