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Related Concept Videos

Larynx01:21

Larynx

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The human larynx, often referred to as the voice box, is an intricate organ located in the neck. It serves as a pathway for air to enter the lungs during respiration and is an essential component of voice production.
Anatomy of the Larynx
The larynx consists of various components, including cartilage, muscles, and vocal cords. Its structure includes three large unpaired cartilages—the thyroid, cricoid, and epiglottis—and three smaller paired cartilages—the arytenoids,...
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Harnessing machine learning in diagnosing complex hoarseness cases.

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Summary

Machine learning models can now analyze voice recordings to detect vocal fold pathologies, improving diagnosis for conditions like Laryngeal Dystonia and enhancing patient care.

Keywords:
HuBERT modelLaryngeal DystoniaMachine learningSaarbruecken Voice DatabaseSpasmodic dysphoniaVoice disorders diagnosis

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Area of Science:

  • Computational linguistics
  • Medical diagnostics
  • Artificial intelligence in healthcare

Background:

  • Traditional vocal fold pathology diagnosis relies on laryngologist expertise and direct visualization.
  • A need exists for supplementary, accessible diagnostic methods.

Purpose of the Study:

  • To develop and evaluate a machine-learning algorithm for voice analysis.
  • To distinguish between healthy and hoarse voices.
  • To identify specific laryngeal disorders from voice recordings.

Main Methods:

  • Utilized transfer learning on the HuBERT model with the Saarbruecken Voice Database.
  • Employed a two-stage machine learning approach: binary classification (healthy vs. hoarse) and multi-class classification (specific disorders).
  • Analyzed data from 2103 sessions, encompassing diverse pathologies and healthy individuals.

Main Results:

  • The binary classifier achieved 82% accuracy in differentiating healthy from pathological voices.
  • The multi-class classifier demonstrated over 93% accuracy in identifying specific laryngeal disorders, notably Laryngeal Dystonia.
  • Laryngeal Dystonia remains a diagnostic challenge, highlighting the potential of AI-assisted methods.

Conclusions:

  • Machine learning effectively categorizes voice samples for distinct pathologies.
  • This AI-driven approach can enhance patient triage and streamline diagnostic processes.
  • The method shows promise for improving care, especially for complex conditions like Laryngeal Dystonia.