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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Automatic Classification of Healthy Subjects and Patients With Essential Vocal Tremor Using Probabilistic

Achuth Rao Mv1, B K Yamini2, J Ketan3

  • 1Electrical Engineering, Indian Institute of science (IISc), Bangalore, 560012, India.

Journal of Voice : Official Journal of the Voice Foundation
|February 13, 2021
PubMed
Summary

This study presents a robust method for classifying essential voice tremor (EVT) using advanced signal processing. The approach enhances accuracy in noisy conditions, aiding clinical diagnosis of this voice disorder.

Keywords:
Empirical mode decompositionEssential voice tremor glottal closure instantsProbabilistic source filter modelSupport vector machineTremor

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Area of Science:

  • Speech processing
  • Biomedical engineering
  • Clinical diagnostics

Background:

  • Essential voice tremor (EVT) is a laryngeal muscle dyscoordination causing voice frequency fluctuations.
  • Accurate classification of EVT is crucial for clinical management.
  • Traditional methods struggle with noise interference in voice analysis.

Purpose of the Study:

  • To develop a noise-robust automatic classification system for essential voice tremor (EVT).
  • To improve the estimation of high-resolution pitch contours (HPRC) for better EVT detection.
  • To evaluate the proposed method's performance against baseline techniques under various noise conditions.

Main Methods:

  • Utilized a probabilistic source filter model for noise-robust glottal closure instant (GCI) detection.
  • Estimated high-resolution pitch contours (HPRC) from GCIs for feature extraction.
  • Employed Empirical Mode Decomposition for feature extraction and a Support Vector Machine (SVM) classifier.

Main Results:

  • The proposed method demonstrated superior performance in classifying essential voice tremor (EVT) compared to baseline techniques.
  • Effectiveness was validated across eight additive noise conditions and six signal-to-noise ratio (SNR) levels.
  • The noise-robust GCI detection and EMD-based features significantly improved classification accuracy.

Conclusions:

  • The developed method offers a reliable approach for automatic essential voice tremor (EVT) classification, even in noisy environments.
  • This technique has the potential to be a valuable tool for clinicians in diagnosing voice disorders.
  • Noise-robust signal processing is critical for accurate assessment of voice tremor.