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The innovation of touch-tone telephony revolutionized the telecommunications industry by replacing the traditional rotary dial with a dual-tone multi-frequency (DTMF) signaling system. This system uses a matrix-style keypad with buttons arranged in four rows and three columns, creating 12 distinct signals each assigned to a pair of frequencies. Each button press results in a simultaneous generation of two sinusoidal tones – one from a low-frequency group (697 to 941 Hz) and one from a...

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Exploring Silent Speech Interfaces Based on Frequency-Modulated Continuous-Wave Radar.

David Ferreira^1,2, Samuel Silva^1,2, Francisco Curado^1,2

¹Department of Electronics, Telecommunications & Informatics, University of Aveiro, 3810-193 Aveiro, Portugal.

Sensors (Basel, Switzerland)

|January 22, 2022

Summary

This summary is machine-generated.

This study explores contactless continuous-wave radar for silent speech interfaces (SSI). Radar achieved high accuracy in recognizing words, showing promise for non-acoustic communication technologies.

Keywords:

European Portuguese continuous-wave radar machine learning silent speech

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Area of Science:

Human-computer interaction
Signal processing
Biomedical engineering

Background:

Audible speech communication faces limitations due to environmental, contextual, or health factors.
Silent Speech Interfaces (SSI) offer alternatives but often have intrusive or technical limitations.
Contactless sensing is needed for practical and private SSI applications.

Purpose of the Study:

To investigate the feasibility of continuous-wave radar for Silent Speech Interface (SSI) development.
To evaluate radar-based speech recognition accuracy for European Portuguese words.
To assess the potential of radar for non-acoustic communication.

Main Methods:

Acquired a corpus of 13 European Portuguese words from four speakers, with repeat sessions for some.
Employed speaker-dependent models using Bagging (BAG) and Linear Regression (LR) classifiers with 5-fold cross-validation.
Conducted speaker- and session-independent experiments to assess generalization.

Main Results:

Speaker-dependent models achieved high accuracies: 84.50% (BAG) and 88.00% (LR).
Speaker- and session-independent recognition accuracies reached 81.79% and 81.80%, respectively.
Demonstrated the viability of radar for recognizing silent speech patterns.

Conclusions:

Continuous-wave radar shows significant potential as a non-intrusive technology for Silent Speech Interfaces.
The achieved accuracies support further research into radar-based silent speech recognition.
Radar offers a promising avenue for developing robust and private communication systems.