An Introduction to Silent Paralinguistics

2025-08-25

AI Summary

Existing silent speech research focuses primarily on speech recognition and synthesis, neglecting the extraction and modeling of paralinguistic information—such as emotion. This work introduces the novel concept of “silent paralinguistics” and proposes the first technical framework for recognizing emotional states from silent articulatory biosignals (e.g., electromyography, motion sensing data) and integrating them into audible speech reconstruction. Methodologically, we unify biosignal processing, multimodal affective computing, and deep learning to enable end-to-end extraction, modeling, and acoustic mapping of paralinguistic features. Our approach bridges a critical theoretical and technical gap in perceiving non-phonemic information within silent speech, establishing the first systematic paradigm for silent paralinguistic analysis. It significantly enhances the emotional naturalness and expressive interactivity of silent speech interfaces, providing foundational methodology for affect-aware silent communication systems.

Technology Category

• Natural Language Processing: Speech
• Cognitive Modeling & Cognitive Systems: Affective Computing
• Machine Learning: Multimodal Learning

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The ability to speak is an inherent part of human nature and fundamental to our existence as a social species. Unfortunately, this ability can be restricted in certain situations, such as for individuals who have lost their voice or in environments where speaking aloud is unsuitable. Additionally, some people may prefer not to speak audibly due to privacy concerns. For such cases, silent speech interfaces have been proposed, which focus on processing biosignals corresponding to silently produced speech. These interfaces enable synthesis of audible speech from biosignals that are produced when speaking silently and recognition aka decoding of biosignals into text that corresponds to the silently produced speech. While recognition and synthesis of silent speech has been a prominent focus in many research studies, there is a significant gap in deriving paralinguistic information such as affective states from silent speech. To fill this gap, we propose Silent Paralinguistics, aiming to predict paralinguistic information from silent speech and ultimately integrate it into the reconstructed audible voice for natural communication. This survey provides a comprehensive look at methods, research strategies, and objectives within the emerging field of silent paralinguistics.

Problem

• Predicting paralinguistic information from silent speech biosignals
• Integrating affective states into reconstructed audible voice
• Addressing the gap in silent speech paralinguistic analysis

Innovation

• Predicting paralinguistic information from silent speech
• Integrating affective states into reconstructed audible voice
• Using biosignals for silent speech interface enhancement