Bio-integrated systems for silent speech recognition: from advanced bioplatforms to machine learning-assisted biosignal decoding

Abstract

Silent speech interfaces decode intended speech from physiological signals without the need for vocalized sound. These systems provide an alternative modality to voice-based spoken communication, addressing limitations posed by physiological constraints and environmental interferences. This review presents a comprehensive overview of bio-integrated systems for silent speech recognition, with emphasis on their physiological relevance, state-of-the-art hardware designs, signal characteristics, and machine learning (ML)-assisted speech decoding pipelines. Based on the level of physical intrusion into the body, bio-integrated speech interfaces can be categorized into epidermal, intraoral, and surgically embedded systems. Each modality captures distinct physiological signals involved in speech production. The design of bio-integrated systems involves critical trade-offs among recognition accuracy, invasiveness, portability, and robustness. Recent advances in flexible and stretchable electronics have significantly enhanced device comfort, signal quality, and integration level across these modalities. This review also outlines recent progress in ML-assisted signal processing pipelines, including preprocessing, feature extraction, ML model architectures, and evaluation metrics. Both signal-to-text and signal-to-audio approaches are discussed. Finally, application scenarios such as assistive communication, human-machine interaction, and user authentication are introduced, followed by an outlook on current challenges and emerging research directions that position this field for transformative clinical and consumer applications.

Keywords

Silent speech interfaces / bio-integrated systems / epidermal sensors / intraoral devices / implantable neural interfaces / machine learning / human-machine interaction

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