Efficient Sensor Configuration for Accelerometer-Based Silent Speech Interfaces: Regionally Distributed Sensor Placement Strategy Considering Articulatory Dynamics

Abstract Silent speech interfaces (SSIs) decode articulatory activity without audible speech. Although accelerometers can capture facial and neck movements in compact wearable setups, accelerometer-specific sensor placement has not been systematically examined. This study evaluated all combinations of ten accelerometers across the lower face and neck in a 100 English word silent speech classification task with 20 participants. Subject-dependent classification was performed using a convolutional neural network followed by a bidirectional long short-term memory network. Accuracy improved as the number of sensors increased, reaching a plateau with five sensors. At this point, Sensors 1, 4, 5, 7, and 8 achieved the highest accuracy of 92.30 ± 4.64% and were selected as the efficient configuration. Regional coverage comparisons showed that configurations spanning broader functional articulatory regions outperformed those with more restricted coverage. Together, these results support regionally distributed placement as a design strategy for capturing complementary articulatory information in compact accelerometer-based SSI systems.