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Telephone-quality pathological speech classification using empirical mode decomposition.

M F Kaleem1, B Ghoraani, A Guergachi

  • 1Department of Electrical Engineering, Ryerson University, Toronto, Canada. m2kaleem@ryerson.ca

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 19, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a simple method using empirical mode decomposition (EMD) to classify normal and pathological speech over phone lines. The approach achieves high accuracy, enabling cost-effective remote voice pathology assessment.

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Area of Science:

  • Speech Signal Processing
  • Biomedical Engineering
  • Machine Learning for Healthcare

Background:

  • Accurate classification of pathological speech is crucial for remote diagnosis.
  • Existing methods for telephone-quality speech analysis face challenges with noise and data limitations.
  • Empirical Mode Decomposition (EMD) offers a novel approach to feature extraction from complex signals.

Purpose of the Study:

  • To develop and validate a computationally simple and effective methodology for classifying normal versus pathological speech signals transmitted over telephone lines.
  • To assess the performance of the proposed method under varying noise conditions simulating real-world telephone quality.
  • To demonstrate the utility of the methodology for remote voice pathology assessment.

Main Methods:

  • Speech signals were decomposed into intrinsic mode functions using Empirical Mode Decomposition (EMD).
  • Physically meaningful temporal and spectral features were extracted from the intrinsic mode functions.
  • A linear classifier was trained and tested using feature vectors from a database of normal and pathological speakers under simulated telephone quality conditions.
  • Performance was evaluated based on classification accuracy at different signal-to-noise ratios (SNRs).

Main Results:

  • The proposed EMD-based methodology achieved high classification accuracy for telephone-quality speech.
  • Specifically, an accuracy of 89.7% was reported at a signal-to-noise ratio of 30 dB.
  • This performance represents a significant improvement over previously reported results for the same classification task.
  • The extracted features were found to be unique and physically meaningful for distinguishing speech types.

Conclusions:

  • The developed methodology based on EMD is effective for classifying normal and pathological speech signals under telephone quality constraints.
  • The approach demonstrates significant potential for cost-effective remote voice pathology assessment via telephone channels.
  • The findings highlight the utility of EMD in extracting discriminative features from noisy, low-quality speech data.