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.
We present E-MASK, a mask-shaped interface for silent speech interaction. 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. 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.79.1%, and to classify 6 types of facial expressions and actions with an accuracy of 84.7%.