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Automatic voice disorder detection using tree-based ensemble model
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  • Weihao Zhuang,
  • Junhong Zhang,
  • Wentian Xu,
  • Qi Xi,
  • Jing Wang,
  • Xueyuan Zhang
Weihao Zhuang
South China Normal University
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Junhong Zhang
South China Normal University
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Wentian Xu
South China Normal University
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Qi Xi
South China Normal University

Corresponding Author:[email protected]

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Jing Wang
South China Normal University
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Xueyuan Zhang
Sun Yat-Sen Memorial Hospital
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Abstract

Diagnostic modeling of voice disorders is important to improve the efficiency of patient diagnosis. However, due to sample imbalance and complexity of voice data analysis, basic acoustic features and traditional machine learning methods are not effective in predicting voice status. This letter proposes a three-layer ensemble learning model with two innovations: 1) At the feature level, acoustic features are expanded using Multilayer Perceptron and SincNet; 2) Structurally, a tree-based ensemble learning model is proposed that utilizes XGBoost for feature selection and feature transformation, with LightGBM as the classifier. Experiments have shown that the proposed method in this paper outperforms traditional machine learning models.
27 Mar 2024Submitted to Electronics Letters
30 Mar 2024Assigned to Editor
30 Mar 2024Submission Checks Completed
30 Mar 2024Review(s) Completed, Editorial Evaluation Pending
11 Apr 2024Reviewer(s) Assigned