Hearables, wearable earphone-type devices, are often operated via voice commands. However, voice control faces limitations such as sensitivity to environmental noise, difficulty in silent environments, and privacy concerns. Silent speech interaction (SSI) has been proposed to address these issues, with methods using IMUs and ultrasound showing promise. Yet, SSI using ear canal pressure remains underexplored. In this study, we propose a novel SSI method that recognizes silent speech by analyzing pressure changes in the ear canal, measured by a built-in barometric sensor. These signals are classified using machine learning techniques such as SVM, DTW, and kNN. Experiments were conducted with seven participants using five predefined commands related to music control. The proposed method achieved an average recognition accuracy of 72.0%. This low-power approach could be integrated with existing SSI methods, enabling adaptive switching depending on context while improving usability in diverse environments.