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Artifact removal algorithm for an EMG-based Silent Speech Interface.

Michael Wand, Adam Himmelsbach, Till Heistermann

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|October 11, 2013

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This study enhances silent speech recognition by improving electromyographic (EMG) signal quality. Independent Component Analysis effectively removes artifacts, significantly reducing word error rates for silent communication.

Area of Science:

Biomedical Engineering
Signal Processing
Human-Computer Interaction

Background:

Silent Speech Interfaces (SSI) utilize electromyographic (EMG) signals from articulatory muscles for silent communication.
EMG signal quality is often degraded by artifacts, hindering the performance of SSI systems.
Effective artifact removal is crucial for reliable silent speech recognition.

Purpose of the Study:

To improve the quality of EMG signals used in Silent Speech Interfaces.
To develop an automated method for detecting and removing artifacts from high-dimensional EMG data.
To evaluate the impact of artifact removal on the performance of a silent speech recognizer.

Main Methods:

EMG signals were captured using electrode arrays with multiple measuring points.

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High-dimensional EMG data was processed using Independent Component Analysis (ICA).

Artifact components were automatically identified and removed from the processed signals.

Main Results:

The proposed artifact removal method significantly improved EMG signal quality.
A relative reduction of 9.9% in Word Error Rate (WER) was achieved on a development corpus.
A further relative reduction of 13.9% in WER was observed on an evaluation corpus.

Conclusions:

Automated artifact removal using ICA is an effective technique for enhancing EMG signal quality in SSI.
Improved signal quality directly translates to a significant reduction in word error rates for silent speech recognition.
This method offers a promising approach for advancing the capabilities and reliability of Silent Speech Interfaces.