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Updated: Mar 14, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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Deep Learning Classification of Multicategory Voice Disorders Using Multi-Vowel Mel Spectrograms.

Yimiao Wang1, Shuaichi Ma1,2, Chen Zhao1,2

  • 1ENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, People's Republic of China.

Ear, Nose, & Throat Journal
|March 13, 2026
PubMed
Summary

A new deep learning model accurately classifies voice disorders from non-invasive recordings. This AI tool shows promise for screening high-risk laryngeal lesions, improving upon traditional diagnostic methods.

Keywords:
Mel spectrogramartificial intelligencedeep learninglaryngeal lesionsvocal fold mobility disordersvoice disorders

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Area of Science:

  • Artificial Intelligence in Medicine
  • Bioacoustics
  • Medical Diagnostics

Background:

  • Voice disorders are prevalent, stemming from vocal fold lesions or mobility issues.
  • Current diagnostic methods, such as laryngoscopy, are mildly invasive.
  • There is a need for non-invasive diagnostic tools for voice disorders.

Purpose of the Study:

  • To develop and validate a deep learning (DL) model for automatic classification of voice disorders.
  • The model utilizes non-invasive voice recordings for diagnosis.
  • To assess the model's performance on multi-class and binary classification tasks.

Main Methods:

  • A retrospective analysis of 897 patients and healthy controls was conducted.
  • Sustained Mandarin vowels were recorded and converted to Mel spectrograms.
  • A ResNet and feature pyramid network-based DL model was trained and evaluated.

Main Results:

  • The 3-class task (normal, lesions, mobility disorders) achieved an accuracy of 0.795.
  • The binary task (high-risk vs. benign lesions) achieved an accuracy of 0.826.
  • The model demonstrated high sensitivity (0.933) for detecting high-risk laryngeal lesions.

Conclusions:

  • A DL model analyzing multi-vowel Mel spectrograms can non-invasively classify voice disorders.
  • The model achieves clinically meaningful accuracy.
  • It shows significant potential as a screening tool for high-risk laryngeal lesions.