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Multidirectional regression (MDR)-based features for automatic voice disorder detection.

Ghulam Muhammad1, Tamer A Mesallam, Khalid H Malki

  • 1Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia. ghulam@ksu.edu.sa

Journal of Voice : Official Journal of the Voice Foundation
|November 27, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new feature extraction method for automatic speech recognition (ASR) to detect voice pathology. The novel approach significantly improves accuracy in identifying voice disorders.

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Area of Science:

  • Speech processing
  • Biomedical engineering
  • Computational linguistics

Background:

  • Objective voice pathology assessment is increasingly important.
  • Automatic speech recognition (ASR) systems are utilized for voice disorder detection.
  • Current methods may lack comprehensive feature extraction.

Purpose of the Study:

  • To develop a novel feature extraction method for ASR systems.
  • To incorporate voiced/unvoiced parts and voice onset/offset characteristics.
  • To enhance the detection of voice pathology.

Main Methods:

  • Analyzed speech samples from 70 dysphonic patients and 50 normal subjects.
  • Utilized Arabic spoken digits (1-10) as input.
  • Embedded the proposed feature extraction into an ASR system with a Gaussian Mixture Model (GMM) classifier.

Main Results:

  • Achieved 97.48% accuracy in text-independent mode and over 99% in text-dependent mode.
  • The novel method demonstrated superior performance compared to Mel Frequency Cepstral Coefficients (MFCC).
  • Successfully detected voice disorders using the developed ASR system.

Conclusions:

  • Incorporating voice onset and offset information enhances automatic voice disorder detection.
  • The proposed feature extraction method offers an efficient approach for ASR-based pathology detection.
  • This technique holds promise for objective and accurate voice disorder diagnosis.