Intelligent Classification of Electrolaryngograph Signals

Abstract

This paper describes a prototype system for the intelligent classification of electrolaryngograph (EGG) signals in order to provide an objective assessment of voice quality in patients at different stages of recovery after treatment for larynx cancer. The system extracts salient short-term and long-term time-domain and frequency-domain parameters from EGG signals taken from male patients steadily phonating the vowel /i/. The quality of these voices was also independently assessed by a Speech and Language Therapist (SALT) according to their 7-point ranking of subjective voice quality. These data were used to train and test a Multi-layer Perceptron (MLP) neural network to classify EGG signals in terms of voice quality. Several MLP configurations were investigated using various combinations of these signal parameters, and the best results were obtained using a combination of short-term and long-term parameters, for which an accuracy of 92% was achieved. It is envisaged that this system could be used as a valuable aid to the SALT during clinical evaluation of voice quality.

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA409955

Entities

People

  • C. J. Moore
  • M. Mcgillion
  • R. T. Ritchings

Organizations

  • University of Salford

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Accuracy
  • Acoustic Signals
  • Algorithms
  • Classification
  • Computer Science
  • Data Processing
  • Fast Fourier Transforms
  • Frequency
  • Frequency Domain
  • Information Science
  • Intelligent Systems
  • Language
  • Military Research
  • Neural Networks
  • Spectra
  • Time Domain

Readers

  • Neural Network Machine Learning.
  • Speech Processing/Speech Recognition.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Information Retrieval
  • AI & ML - Neural Networks