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Related Concept Videos

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Related Experiment Video

Updated: Jul 6, 2026

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis
05:48

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Published on: August 9, 2024

EMG-based speech recognition using hidden markov models with global control variables.

Ki-Seung Lee1

  • 1Department of Electronic Engineering, Konkuk University, 1 Hwayang-dong, Gwangjin-gu, Seoul 143-701, Korea. kseung@konkuk.ac.kr

IEEE Transactions on Bio-Medical Engineering
|March 13, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces an automatic speech recognition system using only electromyogram (EMG) signals from facial muscles. The novel hidden Markov model (HMM) approach achieved 87.07% accuracy, outperforming independent models.

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Related Experiment Videos

Last Updated: Jul 6, 2026

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis
05:48

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Published on: August 9, 2024

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Speech Technology

Background:

  • A strong correlation exists between human voice production and articulatory facial muscle movements.
  • Surface electromyogram (EMG) signals offer a direct measure of muscle activity during speech.

Purpose of the Study:

  • To develop an automatic speech recognition (ASR) system utilizing solely surface EMG signals.
  • To model multichannel EMG observation sequences within a hidden Markov model (HMM) framework.
  • To enhance speech recognition accuracy by accounting for dependencies between EMG signals.

Main Methods:

  • Implementation of a hidden Markov model (HMM) framework for EMG signal sequences.
  • Development of a state observation density model incorporating a global control variable to capture inter-signal dependencies.
  • Efficient model training using a maximum likelihood criterion.
  • Acquisition of EMG signals from three articulatory facial muscles for 60 isolated words.

Main Results:

  • The proposed HMM-based system achieved a recognition accuracy of up to 87.07% for isolated words.
  • The model incorporating inter-signal dependencies demonstrated superior performance compared to an independent probabilistic model.
  • Preliminary study validates the efficacy of EMG-based speech recognition.

Conclusions:

  • Surface EMG signals can be effectively utilized for automatic speech recognition.
  • The proposed HMM with a global control variable offers a robust approach for modeling correlated EMG signals.
  • This EMG-based ASR system shows significant potential for applications where traditional speech recognition methods are challenging.