Journal of Guangxi Normal University(Natural Science Edition) ›› 2018, Vol. 36 ›› Issue (4): 34-41.doi: 10.16088/j.issn.1001-6600.2018.04.005

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Identification of Pathological Voice of Different Levels Based on Random Forest

XU Yuanjing, HU Weiping*   

  1. College of Electronic Engineering, Guangxi Normal University, Guilin Guangxi 541004,China
  • Received:2018-03-29 Published:2018-10-20

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Abstract: In order to identify the different degrees of pathological voice recognition, a method based on random forest recognition is proposed in this paper. The normal, moderate, and severe pathological voices are identified separately and compared with the recognition results of GMM. The experimental results show that compared with GMM, random forest method has higher classification accuracy, robustness, and better recognition results. The highest recognition rates of normal, moderate, and severe voices are 98.04%, 86.84%, and 83.33%, respectively. It provides a reference for further research on the classification of pathological voice.

Key words: pathological voice, random forest, Gaussian mixed model, robustness

CLC Number: 

  • TP391.4
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