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Related Experiment Videos

Spectral pattern recognition of improved voice quality

H Rihkanen1, L Leinonen, T Hiltunen

  • 1Department of Otorhinolaryngology, Helsinki University Central Hospital, Finland.

Journal of Voice : Official Journal of the Voice Foundation
|December 1, 1994
PubMed
Summary

A self-organizing map accurately identified voice quality disorders by analyzing acoustic features. This neural network approach detected improvements in voice quality after surgery, correlating with expert perception.

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Area of Science:

  • Computational intelligence
  • Speech science
  • Biomedical engineering

Background:

  • Voice disorders, such as those from insufficient glottal closure, significantly impact quality of life.
  • Objective acoustic analysis is crucial for diagnosing and monitoring voice disorders.
  • Perceptual ratings by experts are the gold standard but can be subjective.

Purpose of the Study:

  • To apply a self-organizing map (SOM) for spectral pattern recognition of voice quality.
  • To correlate the SOM's analysis with expert perceptual ratings of voice samples.
  • To evaluate the SOM's ability to detect changes in voice quality, including post-operative improvements.

Main Methods:

  • Utilized a self-organizing map (SOM), a type of neural network, for spectral pattern recognition.

Related Experiment Videos

  • Analyzed voice samples (/a/ phonations) from 34 subjects (15 postsurgical, 19 controls).
  • Computed an acoustic feature map and determined sample locations relative to a normal reference, comparing distances with expert perceptual ratings.

    • Main Results:

    • SOM map locations corresponded directly to the degree of audible voice disorder.
    • Normal voice samples clustered near the normal reference on the map.
    • Dysphonic voice samples were located further from the normal reference, aligning with perceptual judgments.
    • The SOM detected perceived voice quality improvements in post-operative patient samples.

    Conclusions:

    • Self-organizing maps are effective tools for objective voice quality analysis.
    • SOMs can accurately differentiate between normal and disordered voices based on acoustic features.
    • This method shows promise for monitoring treatment efficacy and detecting subtle voice changes.