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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Published on: July 22, 2025

Research on Pathological Voice Recognition Based on XGBoost.

Liqin Wang1, Haibing Chen2, Xiaoyang Gong2

  • 1Department of Otorhinolaryngology, The First Affiliated Hospital with Nanjing Medical University; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing University of Aeronautics and Astronautics.

Journal of Visualized Experiments : Jove
|June 15, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for pathological voice recognition using acoustic analysis and machine learning. XGBoost achieved superior accuracy in identifying voice disorders compared to SVM.

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Area of Science:

  • Speech analysis
  • Biomedical engineering
  • Machine learning

Background:

  • Increasing prevalence of voice disorders necessitates advanced diagnostic tools.
  • Acoustic detection offers an objective, non-invasive approach to voice disorder analysis.
  • Speech signal analysis is a key area for pathological voice recognition research.

Purpose of the Study:

  • To develop and evaluate a machine learning-based classifier for pathological voice recognition.
  • To explore the efficacy of nonlinear dynamic parameters extracted via wavelet packet analysis for voice disorder classification.
  • To compare the performance of the XGBoost algorithm against Support Vector Machines (SVM) for this task.

Main Methods:

  • Utilized 101 continuous /a/ vowels from the German SVD database.
  • Applied wavelet packet technology for time-frequency analysis.
  • Extracted four nonlinear dynamic parameters: approximate entropy, sample entropy, fuzzy entropy, and permutation entropy.
  • Employed the XGBoost machine learning algorithm for classification, validated with five-fold cross-validation and ROC curve analysis.

Main Results:

  • The XGBoost classifier achieved an accuracy of 0.857, an F1 score of 0.875, and an AUC value of 0.944.
  • These performance metrics surpassed those obtained using a Support Vector Machine (SVM) classifier.
  • Nonlinear dynamic parameters proved effective features for pathological voice recognition.

Conclusions:

  • XGBoost demonstrates superior performance in pathological voice recognition compared to SVM.
  • Wavelet packet-derived nonlinear dynamic features are valuable for classifying voice disorders.
  • The proposed method offers a promising approach for objective and non-invasive diagnosis of pathological voices.