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Related Experiment Videos

A nonlinear operator-based speech feature analysis method with application to vocal fold pathology assessment

J H Hansen1, L Gavidia-Ceballos, J F Kaiser

  • 1Department of Electrical Engineering, Duke University, Durham, NC 27708-0281, USA. jhlh@ee.duke.edu

IEEE Transactions on Bio-Medical Engineering
|March 24, 1998
PubMed
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A novel nonlinear signal processing method offers a new way to assess vocal fold pathologies. This technique bypasses the need for direct glottal flow estimation, improving diagnostic capabilities for various laryngeal conditions.

Area of Science:

  • Speech Processing
  • Biomedical Engineering
  • Laryngeal Pathology Assessment

Background:

  • Traditional laryngeal pathology assessment relies on linear speech processing, assuming complete glottal closure, which is often unachievable for patients with vocal fold pathologies.
  • Existing methods require direct glottal flow waveform estimation, posing limitations for accurate diagnosis in cases of incomplete glottal closure.

Purpose of the Study:

  • To introduce a novel nonlinear signal processing approach for laryngeal pathology assessment.
  • To develop a method that does not necessitate direct glottal flow waveform estimation or complete glottal closure.
  • To propose a computationally efficient and noninvasive technique for vocal fold pathology detection.

Main Methods:

  • Employed a nonlinear signal processing technique utilizing a differential Teager energy operator and the energy separation algorithm.

Related Experiment Videos

  • Extracted amplitude modulation (AM) and frequency modulation (FM) from filtered speech recordings.
  • Introduced a new speech measure based on the autocorrelation envelope of the AM response.

    • Main Results:

    • The proposed nonlinear approach demonstrated impressive detection performance for muscular tension dysphonias.
    • The method proved computationally attractive, requiring only a small time window of speech samples.
    • Achieved effective vocal fold pathology assessment without requiring complete glottal closure or direct glottal flow estimation.

    Conclusions:

    • The developed nonlinear method provides a fast, effective, and noninvasive digital speech processing technique for vocal fold pathology assessment.
    • This approach overcomes the limitations of traditional linear methods by not requiring direct glottal flow estimation or complete glottal closure.
    • Highlights the potential of nonlinear signal processing techniques in advancing speech pathology assessment.