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Updated: Mar 27, 2026

fMRI Mapping of Brain Activity Associated with the Vocal Production of Consonant and Dissonant Intervals

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Soft Multiaxial Strain Mapping Interface with AI-Driven Decoding for Silent Speech in Noise.

Sunguk Hong¹, Junyoung Yoo², Sung-Min Park^1,2,3,4,5,6

¹Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, South Korea.

Cyborg and Bionic Systems (Washington, D.C.)

|March 25, 2026

Summary

This summary is machine-generated.

This study introduces a novel silent speech interface (SSI) using computer vision to detect throat muscle strain, reconstructing clear speech even in noisy environments. This innovation offers a reliable alternative for communication where traditional microphones fail.

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Area of Science:

Biomedical Engineering
Signal Processing
Machine Learning

Background:

Traditional microphones struggle in noisy environments.
Silent speech interfaces (SSIs) offer an alternative for clear audio capture.
Existing wearable sensors for SSIs have limitations in scalability and reliability.

Purpose of the Study:

To develop a reconceptualized SSI using computer vision to monitor throat muscle strain.
To enable clear alphabetic communication via voice reconstruction under extreme noise conditions.
To enhance the robustness and applicability of SSIs in real-world scenarios.

Main Methods:

Integration of a computer vision-based optical strain (CVOS) sensor with deep learning for voice reconstruction.
Utilizing a soft silicone substrate with micromarkers and a camera for high-sensitivity strain pattern capture.
Implementing physics-based automated baseline calibration and content-adaptive temporal attention for robust inference.

Main Results:

The CVOS sensor demonstrated high-sensitivity marker detection and reliable strain pattern capture.
The CVOS-based SSI achieved clear alphabetic communication in extreme noise conditions.
Real-time adaptive signal processing compensated for anatomical variability, ensuring robustness.

Conclusions:

The proposed CVOS-based SSI offers a scalable, reliable, and practical solution for clear speech communication in noisy environments.
The synergy of optimized algorithms and interface design enables effective alphabet-based communication.
The system's performance was validated in real-world noisy scenarios, confirming its practical applicability.