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Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
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A Voice Disease Detection Method Based on MFCCs and Shallow CNN.

Xiaoping Xie1, Hao Cai2, Can Li2

  • 1The State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, China; Shenzhen Research Institute of Hunan University, Shenzhen, China.

Journal of Voice : Official Journal of the Voice Foundation
|October 27, 2023
PubMed
Summary

This study introduces a novel voice disease detection method using Mel Frequency Cepstrum Coefficient (MFCC) parameters and a convolutional neural network (CNN). The approach achieves high accuracy for diagnosing hoarseness, improving upon existing methods.

Keywords:
Deep learningDisease detectionMFCCsPathological voice disorder

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Area of Science:

  • Medical Informatics
  • Speech Processing
  • Artificial Intelligence in Healthcare

Background:

  • The incidence of voice diseases is rising, necessitating advanced diagnostic tools.
  • Remote diagnosis software represents a significant trend in healthcare technology.
  • Common causes of hoarseness include spasmodic dysphonia, vocal cord paralysis, vocal nodules, and vocal cord polyps.

Purpose of the Study:

  • To develop and validate an accurate and efficient voice disease detection method for clinical application.
  • To leverage machine learning for the automated classification of pathological voice conditions.

Main Methods:

  • Collected voice samples from 352 patients in collaboration with Xiangya Hospital.
  • Extracted Mel Frequency Cepstrum Coefficient (MFCC) parameters as voice features.
  • Developed a classification model integrating MFCC features with a single-layer Convolutional Neural Network (CNN).

Main Results:

  • Achieved a highest accuracy of 92% on clinical datasets, surpassing previous research.
  • Validated generalization ability using the Advanced Voice Function Assessment Database (AVFAD), reaching 98% accuracy.
  • Demonstrated significant improvements in both accuracy and computational efficiency for voice disease detection.

Conclusions:

  • The proposed MFCC and CNN-based method offers a highly accurate and efficient solution for voice disease detection.
  • This approach holds significant potential for remote diagnosis and widespread clinical application.
  • The method shows strong performance on both clinical and standard datasets, indicating robust generalization capabilities.