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A HTK-based Method for Detecting Vocal Fold Pathology.

Vahid Majidnezhad1

  • 1Department of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran.

Acta Informatica Medica : AIM : Journal of the Society for Medical Informatics of Bosnia & Herzegovina : Casopis Drustva Za Medicinsku Informatiku Bih
|November 15, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a novel acoustic analysis method for detecting vocal fold pathology using continuous Russian speech, achieving 90% accuracy. This approach offers a promising auxiliary tool for medical diagnostics.

Keywords:
Automatic Speech Recognition (ASR)Hidden Markov model Toolkit (HTK)Russian digitsvocal fold pathology

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Area of Science:

  • Speech analysis
  • Medical diagnostics
  • Acoustic phonetics

Background:

  • Acoustic analysis methods for vocal fold pathology detection are emerging.
  • Existing methods predominantly focus on phoneme-based detection.
  • Detection based on continuous speech remains an under-researched area.

Purpose of the Study:

  • To develop a novel method for vocal fold pathology detection using continuous speech.
  • To apply this method to Russian digits for Belorussian populations.
  • To evaluate the accuracy of the proposed detection technique.

Main Methods:

  • Utilized the Hidden Markov Model Toolkit (HTK) for acoustic analysis.
  • Developed a three-state Hidden Markov Model (HMM) for phoneme modeling.
  • Applied the method to continuous Russian digits (1-10).

Main Results:

  • The proposed method achieved a 90% detection accuracy rate.
  • Experimental results demonstrate the effectiveness of the continuous speech approach.
  • The method shows high potential for accurate pathology identification.

Conclusions:

  • This work presents a pioneering approach to vocal fold pathology detection using continuous Russian digits.
  • The high accuracy suggests its utility as an auxiliary diagnostic tool.
  • The method is recommended for implementation in medical centers.