Bibliographic Details
| Title: |
Research on Pathological Voice Recognition Based on XGBoost. |
| Authors: |
Wang L; Department 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., Chen H; Department of Otorhinolaryngology, The First Affiliated Hospital with Nanjing Medical University., Gong X; Department of Otorhinolaryngology, The First Affiliated Hospital with Nanjing Medical University., Shi Y; Department of Otorhinolaryngology, The First Affiliated Hospital with Nanjing Medical University., Lu Z; Department of Otorhinolaryngology, The First Affiliated Hospital with Nanjing Medical University., Zhang L; Department of Otorhinolaryngology, The First Affiliated Hospital with Nanjing Medical University., Chen X; Department of Otorhinolaryngology, The First Affiliated Hospital with Nanjing Medical University., Chen D; Department of Otorhinolaryngology, The First Affiliated Hospital with Nanjing Medical University., Zhou H; Department of Otorhinolaryngology, The First Affiliated Hospital with Nanjing Medical University., Cheng L; Department of Otorhinolaryngology, The First Affiliated Hospital with Nanjing Medical University; Department of Allergology & Clinical Allergy Center, The First Affiliated Hospital with Nanjing Medical University; chenglei@jsph.org.cn. |
| Source: |
Journal of visualized experiments : JoVE [J Vis Exp] 2026 May 29 (231). Date of Electronic Publication: 2026 May 29. |
| Publication Type: |
Journal Article; Video-Audio Media; Research Support, Non-U.S. Gov't |
| Language: |
English |
| Journal Info: |
Publisher: MYJoVE Corporation Country of Publication: United States NLM ID: 101313252 Publication Model: Electronic Cited Medium: Internet ISSN: 1940-087X (Electronic) Linking ISSN: 1940087X NLM ISO Abbreviation: J Vis Exp Subsets: MEDLINE |
| Imprint Name(s): |
Original Publication: [Boston, Mass. : MYJoVE Corporation, 2006]- |
| MeSH Terms: |
Pattern Recognition, Automated*/methods , Voice Disorders*/diagnosis , Boosting Machine Learning Algorithms*, Humans |
| Abstract: |
With the continuous growth of human social communication, the number of people suffering from voice disorders is also increasing. Due to the objective and non-invasive advantages of acoustic detection methods for pathological voice, the use of speech signal analysis for pathological voice recognition has become a research hotspot. This article first selected 101 continuous vowels /a/ from the German SVD database as the research object. Secondly, using wavelet packet technology for time-frequency analysis, four nonlinear dynamic parameters, namely approximate entropy, sample entropy, fuzzy entropy, and permutation entropy, are extracted from the sub signals as the feature parameter set for the pathological voice classifier. Finally, the machine learning algorithm XGBoost is selected as the pattern recognition method to establish a pathological voice classifier, and the classification performance is verified using five fold cross validation and ROC curve. Experimental results have shown that the accuracy of XGBoost's pathological voice classifier is 0.857, the F1 score is 0.875, and the AUC value is 0.944, all of which are higher than the classifier constructed by SVM, indicating that XGBoost has better performance in pathological voice recognition. |
| Entry Date(s): |
Date Created: 20260615 Date Completed: 20260615 Latest Revision: 20260730 |
| Update Code: |
20260731 |
| DOI: |
10.3791/68784 |
| PMID: |
42296181 |
| Database: |
MEDLINE |