Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jun 22, 2026

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

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

Published on: August 9, 2024

Improved phoneme-based myoelectric speech recognition.

Quan Zhou¹, Ning Jiang, Kevin Englehart

¹Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada. t8v11@unb.ca

IEEE Transactions on Bio-Medical Engineering

|June 19, 2009

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

A Qualitative Exploration of EMG Visual Feedback for Spinal Cord Injury Rehabilitation.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]·2025

Same author

Generalizing Upper Limb Force Modeling With Transfer Learning: A Multimodal Approach Using EMG and IMU for New Users and Conditions.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2024

Same author

Compression of EMG Signals Using Deep Convolutional Autoencoders.

IEEE journal of biomedical and health informatics·2022

Same author

A Multiday Evaluation of Real-Time Intramuscular EMG Usability with ANN.

Sensors (Basel, Switzerland)·2020

Same author

A Deep Transfer Learning Approach to Reducing the Effect of Electrode Shift in EMG Pattern Recognition-Based Control.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2019

Same author

EMG Pattern Recognition for Persons with Cervical Spinal Cord Injury.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]·2019

This study presents an improved myoelectric signal (MES) speech recognition system. The enhanced system accurately recognizes new words without retraining, overcoming previous performance drops and promising robust large-vocabulary applications.

Area of Science:

Biomedical Engineering
Signal Processing
Human-Computer Interaction

Background:

Traditional speech recognition struggles with new words, leading to performance degradation.
Phoneme-based systems offer potential for recognizing novel vocabulary without retraining.
Myoelectric signal (MES) based speech recognition is an emerging field for assistive technology.

Purpose of the Study:

To develop an enhanced phoneme-based myoelectric signal (MES) speech recognition system.
To improve accuracy and robustness when recognizing new words in a dynamic vocabulary.
To address the performance degradation issue in existing MES speech recognition systems.

Main Methods:

Preprocessing raw MES data using class-specific rotation matrices for spatial decorrelation.

More Related Videos

Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages

Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages

Published on: March 24, 2023

Related Experiment Videos

Last Updated: Jun 22, 2026

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

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

Published on: August 9, 2024

Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages

Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages

Published on: March 24, 2023

Applying uncorrelated linear discriminant analysis for effective dimensionality reduction.

Utilizing a hidden Markov model (HMM) for phoneme classification and a word classifier for final recognition.

Main Results:

Achieved an average word classification accuracy of 98.533% across six subjects.
Demonstrated significantly improved accuracy when expanding the vocabulary with new words.
Successfully recognized new words without the need for phoneme classifier retraining.

Conclusions:

The proposed MES speech recognition system offers a robust solution for large-vocabulary applications.
The enhancements enable high accuracy even with the addition of new words to the recognized vocabulary.
This approach holds significant promise for developing advanced speech-based human-computer interfaces.