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Related Experiment Video

Updated: Jun 25, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

Flexible Self-Powered Low-Decibel Voice Recognition Mask.

Jianing Li¹, Yating Shi¹, Jianfeng Chen¹

¹Department of Physics, College of Physical Science and Technology, Research Institution for Biomimetics and Soft Matter, Xiamen University, Xiamen 361005, China.

Sensors (Basel, Switzerland)

|May 25, 2024

Summary

A new mask-integrated triboelectric nanogenerator (TENG) captures speech via airflow vibrations. This enables accurate, discreet communication and speaker identification in quiet environments.

Keywords:

human-computer interaction silent communication speech recognition triboelectric nanogenerators vibration sensors

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Area of Science:

Energy Harvesting
Wearable Sensors
Signal Processing

Background:

Silent communication is crucial in environments like libraries and conference rooms.
Existing methods for discreet interaction are limited.
There is a need for unobtrusive speech capture technology.

Purpose of the Study:

To develop a novel single-electrode, contact-separated triboelectric nanogenerator (CS-TENG) for speech signal acquisition.
To integrate the CS-TENG into a mask for unobtrusive data collection.
To achieve accurate speaker content and identity recognition using advanced signal processing.

Main Methods:

Fabrication of a stable, high-frequency CS-TENG.
Integration of the CS-TENG onto a mask for capturing airflow vibrations during speech.
Application of short-time Fourier transform (STFT), Mel-frequency cepstral coefficients (MFCC), and deep learning for signal analysis.
Speaker content and identity recognition using the processed speech data.

Main Results:

The CS-TENG demonstrated high-frequency sensing and long-term stability.
Speech signals were successfully captured through airflow vibrations.
Vocabulary recognition accuracy exceeded 92%.
Speaker identity recognition accuracy exceeded 90%.

Conclusions:

The mask-integrated CS-TENG system enables secure and efficient unobtrusive communication in quiet settings.
This technology has potential applications in smart homes, virtual assistants, and sensitive environments.
The system offers a novel approach to discreet human-computer interaction.