Spectral envelope and periodic component in classification trees for pathological voice diagnostic
Summary
This study identifies pathological voices using spectral envelope features like Linear Predictive Coefficients (LPC) and relative power. A Decision Tree model achieved 94% accuracy in diagnosing healthy and pathological voices.
Area of Science:
- Speech pathology
- Acoustic analysis
- Machine learning for healthcare
Background:
- Pathological voices require accurate diagnostic methods.
- Spectral envelope features offer potential for voice analysis.
- Linear Predictive Coefficients (LPC) are widely used in speech processing.
Purpose of the Study:
- To investigate the effectiveness of spectral envelope features for pathological voice identification.
- To improve voice pathology recognition rates by combining features.
- To develop a diagnostic model for healthy and pathological voices.
Main Methods:
- Extraction of spectral features, including frequency and bandwidth of the first LPC peak.
- Utilizing the Relative Power of the Periodic Component.
- Employing Decision Tree classifiers for feature evaluation and model implementation.
- Testing on healthy voices and five vocal fold pathologies.
Main Results:
- Specific spectral features from LPC were found to contain pathological voice information.
- Combining spectral features with Relative Power enhanced diagnostic capabilities.
- A Decision Tree model correctly diagnosed 94% of subjects.
Conclusions:
- Spectral envelope features, particularly from LPC, are effective for pathological voice detection.
- The proposed method demonstrates high accuracy in classifying healthy and pathological voices.
- This approach offers a promising tool for diagnosing vocal fold pathologies.
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