Xuefu Dong
The University of Tokyo, Tokyo, Japan
Silent speech interaction (SSI) allows users to discreetly input text without using their hands. Existing wearable SSI systems typically require custom devices and are limited to a small lexicon, limiting their utility to a small set of command words. This work proposes ReHearSSE, an earbud-based ultrasonic SSI system capable of generalizing to words that do not appear in its training dataset, providing support for nearly an entire dictionary's worth of words. As a user silently spells words, ReHearSSE uses autoregressive features to identify subtle changes in ear canal shape. ReHearSSE infers words using a deep learning model trained to optimize connectionist temporal classification (CTC) loss with an intermediate embedding that accounts for different letters and transitions between them. We find that ReHearSSE recognizes 100 unseen words with an accuracy of 89.3%.
doi.org/10.1145/3613904.3642095
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2024.acm.org/)