Hidden Markov Model Classification of Myoelectric Signals in Speech

Abstract

A hidden Markov model based classifier is proposed in this paper to perform automatic speech recognition using myoelectric signals from the muscles of vocal articulation. The classifier's resilience to temporal variance is compared to a linear discriminant analysis classifier that was used in a pervious study. Speech recognition was performed, using five channels of myoelectric signals, on isolated words from a 10-word vocabulary. Temporal variance was induced by temporally misaligning data from the test set, with respect to the training set. When compared to the LDA classifier, the hidden Markov model classifier demonstrated a markedly lower variation in classification error due to the temporal misalignment. Characteristics of the hidden Markov model MES classifier suggest that it would effectively complement a conventional acoustic speech recognizer, in a multi-modal speech recognition system.

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA410037

Entities

People

  • A. D. Chan
  • B. Hudgins
  • D. F. Lovely
  • K. Englehart

Organizations

  • University of New Brunswick

Tags

Communities of Interest

  • Biomedical
  • Engineered Resilient Systems

DTIC Thesaurus Topics

  • Acoustic Signals
  • Aircrafts
  • Automated Speech Recognition
  • Biomedical Engineering
  • Classification
  • Coefficients
  • Engineering
  • Hidden Markov Models
  • Human Factors Engineering
  • Jet Aircraft
  • Machine Learning
  • Markov Models
  • Misalignment
  • Neurobehavioral Manifestations
  • Recognition
  • Test Sets
  • Training

Readers

  • Neural Network Machine Learning.
  • Robotics and Automation.
  • Speech Processing/Speech Recognition.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Translation