DECKER: Domain-invariant Embedding for Cross-Keyboard Extraction and Recognition
DECKER introduces a framework to learn acoustic representations of keystrokes that are robust across different devices and keyboards. It combines five components—including normalization, domain adversarial disentanglement, and style randomization—to create key-discriminative yet keyboard-invariant embeddings. This allows the system to accurately identify keystrokes even when the user switches devices or uses a different keyboard.