Silent Speech Interfaces in the Era of Large Language Models: A Comprehensive Taxonomy and Systematic Review

TL;DR

This paper reviews Silent Speech Interfaces (SSIs), highlighting their evolution from traditional methods to modern LLM-based approaches, emphasizing their potential for real-world, wearable applications and addressing ethical considerations.

Contribution

It provides a comprehensive taxonomy of SSI sensing modalities, analyzes the paradigm shift to LLMs for decoding biosignals, and discusses future directions including wearable integration and ethical issues.

Findings

LLMs improve SSI decoding accuracy significantly.

Modern SSIs approach usability thresholds for real-world deployment.

Wearable SSIs are transitioning from lab to everyday devices.

Abstract

Human-computer interaction has traditionally relied on the acoustic channel, a dependency that introduces systemic vulnerabilities to environmental noise, privacy constraints, and physiological speech impairments. Silent Speech Interfaces (SSIs) emerge as a transformative paradigm that bypasses the acoustic stage by decoding linguistic intent directly from the neuro-muscular-articulatory continuum. This review provides a high-level synthesis of the SSI landscape, transitioning from traditional transducer-centric analysis to a holistic intent-to-execution taxonomy. We systematically evaluate sensing modalities across four critical physiological interception points: neural oscillations, neuromuscular activation, articulatory kinematics (ultrasound/magnetometry), and pervasive active probing via acoustic or radio-frequency sensing. Critically, we analyze the current paradigm shift from heuristic signal processing to Latent Semantic Alignment. In this new era, Large Language Models (LLMs) and deep generative architectures serve as high-level linguistic priors to resolve the ``informational sparsity'' and non-stationarity of biosignals. By mapping fragmented physiological gestures into structured semantic latent spaces, modern SSI frameworks have, for the first time, approached the Word Error Rate usability threshold required for real-world deployment. We further examine the transition of SSIs from bulky laboratory instrumentation to ``invisible interfaces'' integrated into commodity-grade wearables, such as earables and smart glasses. Finally, we outline a strategic roadmap addressing the ``user-dependency paradox'' through self-supervised foundation models and define the ethical boundaries of ``neuro-security'' to protect cognitive liberty in an increasingly interfaced world.