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[Recognition and study of pathological voice based on nonlinear dynamics using gaussian mixture model/support vector

Junfen Gao1, Weiping Hu

  • 1Electronic Engineering College, Guangxi Normal Universitye, Guilin 541004, China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
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This study introduces nonlinear dynamics analysis for pathological voice identification, extracting 7 nonlinear features. This advanced method improves upon traditional linear techniques, achieving high accuracy in distinguishing normal from pathological voices.

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Area of Science:

  • Speech Science
  • Nonlinear Dynamics
  • Biomedical Signal Processing

Context:

  • Traditional voice analysis relies on linear methods, potentially overlooking nonlinear characteristics of voice production.
  • Pathological voice identification is crucial for diagnosing various medical conditions.

Purpose:

  • To quantitatively analyze pathological voice using nonlinear dynamics.
  • To extract novel nonlinear features for improved voice analysis.
  • To evaluate the effectiveness of nonlinear methods compared to traditional approaches.

Summary:

  • A nonlinear dynamics analysis method was applied to pathological voice.
  • Seven nonlinear features were extracted: Hurst exponent, time delay, Rényi entropy, Shannon entropy, correlation dimension, Kolmogorov entropy, and largest Lyapunov exponent.
  • Gaussian mixture models and support vector machines were used for classification, achieving high recognition rates (97.22% for normal, 97.30% for pathological voices).

Impact:

  • Demonstrates that nonlinear dynamics analysis can overcome limitations of traditional linear methods in voice analysis.
  • Offers a more comprehensive approach to understanding and identifying pathological voices.
  • Provides a robust method for clinical applications in voice disorder diagnosis.