E-MASK: A Mask-Shaped Interface for Silent Speech Interaction with Flexible Strain Sensors.

We present E-MASK, a mask-shaped interface for silent speech interaction. As face masks have become daily accessories since the COVID-19 pandemic, it is reasonable to utilize a mask as a wearable interface. Unlike conventional speech recognition, we envision that silent speech interaction allows users to access digital services even in crowded public spaces. With flexible and highly sensitive strain sensors, E-MASK presents a new measurement principle for silent speech interactions. We built a dataset of sensor patterns corresponding to 21 fundamental commands of Alexa's operation. All commands were silently spoken by five non-native English speakers. The dataset was used to estimate the silently spoken commands. Estimation accuracies of 84.4% while sitting on a chair and 79.1% while walking on a treadmill were archived. This result suggests that our system provides seamless interaction with digital devices in various situations in daily life, such as walking in a crowd.