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Meta-analysis of voice disorders databases and applied machine learning techniques.

Sidra Abid Syed¹, Munaf Rashid², Samreen Hussain³

¹Biomedical Engineering Department, Ziauddin University Faculty of Engineering Science Technology and Management, Karachi, Pakistan.

Mathematical Biosciences and Engineering : MBE

|December 31, 2020

Summary

This meta-analysis reviews machine learning for voice disorder diagnosis using SVD, MEEI, and AVPD databases. Support Vector Machines (SVM) are most common, with a focus on supervised techniques for accurate pathological speech identification.

Keywords:

AVPD MEEI SVD machine learning technique voice disorder

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Area of Science:

Medical Informatics
Speech Science
Machine Learning

Background:

Voice disorders impact voice production, necessitating advanced diagnostic tools.
Computer-based acoustic analysis offers potential for early detection and tracking of pathological speech.
Accurate acoustic parameter estimation is crucial for reliable voice disorder identification.

Purpose of the Study:

To conduct a meta-analysis of voice disorder databases (SVD, MEEI, AVPD) and applied machine learning techniques.
To systematically review existing literature on computational methods for voice disorder diagnosis.
To identify common algorithms and highlight areas for future research in pathological speech analysis.

Main Methods:

Systematic literature review following PRISMA guidelines.
Keyword-based search across Science Direct, PubMed, and IEEE Xplore databases.
Screening and analysis of 45 eligible studies using voice recordings from SVD, MEEI, and AVPD databases.

Main Results:

Support Vector Machine (SVM) emerged as the most frequently used algorithm for voice disorder detection.
A strong preference for supervised learning techniques over unsupervised methods in clinical diagnosis was observed.
Analysis revealed varying strengths and weaknesses across the SVD, MEEI, and AVPD databases.

Conclusions:

Machine learning, particularly SVM, shows significant promise in the automated diagnosis of voice disorders.
Further research is needed to enhance voice pathology detection, especially utilizing the AVPD database.
Future work should explore a broader range of machine learning techniques and address limitations in current diagnostic approaches.