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  1. Home
  3. Voice Disorders Classification Using Machine Learning: A Scoping Review.
  1. Home
  3. Voice Disorders Classification Using Machine Learning: A Scoping Review.

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Voice disorders classification using machine learning: a scoping review.

Rijul Gupta1, Craig T Jin1, Dhanshree R Gunjawate2,3

  • 1Computing and Audio Research Laboratory, School of Electrical and Information Engineering, The University of Sydney, Sydney, NSW, Australia.

Frontiers in Digital Health
|June 24, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Inconsistent data and methods in machine learning (ML) for voice disorder classification hinder clinical use. Addressing variations in diagnostic labels and testing is crucial for advancing ML applications in healthcare.

Keywords:
digital healthspeech biomarkersvoice AIvoice disorder discriminationvoice disorder recognition

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Area of Science:

  • Medical Informatics
  • Biomedical Engineering
  • Artificial Intelligence in Healthcare

Background:

  • Machine learning (ML) shows promise for voice disorder classification.
  • Clinical application of ML for multi-class voice disorders is limited.
  • Standardization is needed for reliable ML models in voice pathology.

Purpose of the Study:

  • Identify key barriers to clinical application of ML in multi-class voice disorder classification.
  • Analyze patterns in ML techniques for voice disorder classification.
  • Highlight inconsistencies hindering the translation of ML research to clinical practice.

Main Methods:

  • Conducted a comprehensive scoping review of research from 2013 to May 2025.
  • Included articles applying ML for multi-class voice disorder classification.
  • Extracted data on classification classes, databases, input attributes, vocal tasks, labels, and ML techniques.

    • Main Results:

    • Screened 10,401 articles; 80 used ML for multi-class classification.
    • Observed significant variation in databases, diagnostic labels, input data, and ML techniques.
    • Inconsistencies prevent robust comparisons and identification of state-of-the-art solutions.

    Conclusions:

    • Variations in diagnostic labels, data, and testing methods limit ML model comparability and generalization.
    • Lack of consensus in the automated classification pipeline impedes clinical application.
    • Addressing these barriers is essential to realize voice as a biomarker for diseases.