Silent Speech Interface

Silent speech interfaces (SSIs) aim to enable soundless communication by decoding subtle articulatory movements, offering a private and non-invasive alternative to traditional speech recognition. Current research focuses on improving accuracy and robustness through advanced machine learning techniques, including multimodal models that integrate visual and electromyographic data, and efficient neural networks optimized for low-power devices. These advancements are reducing the performance gap between silent and vocal speech recognition, paving the way for practical applications in assistive technologies and human-computer interaction, particularly for individuals with speech impairments or in noisy environments.