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Uncovering Voice Misuse Using Symbolic Mismatch.

Marzyeh Ghassemi1, Zeeshan Syed2, Daryush D Mehta3

  • 1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA.

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|December 24, 2021
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Summary
This summary is machine-generated.

This study introduces a novel method using neck-worn accelerometers to analyze vocal misuse in voice disorder patients. The approach reveals significant behavioral differences, aiding in objective diagnosis and understanding voice therapy effectiveness.

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Area of Science:

  • Biomedical Engineering
  • Speech Science
  • Data Mining

Background:

  • Voice disorders impact millions globally, affecting working-aged individuals.
  • Current diagnostic methods for behavioral voice disorders can be subjective.
  • Objective, data-driven approaches are needed for diagnosis and treatment evaluation.

Purpose of the Study:

  • To present the first large-scale study of vocal misuse using long-term ambulatory accelerometer data.
  • To investigate an unsupervised data mining approach for uncovering latent information about voice misuse.
  • To establish an objective basis for diagnosing behavioral voice disorders and understanding voice therapy.

Main Methods:

  • Collected over 253 days of ambulatory data from 22 subjects using neck-worn accelerometers.
  • Segmented signals into over a hundred million single glottal pulses (vocal fold closures).
  • Employed unsupervised clustering and symbolic mismatch to compare patients and controls, and pre- vs. post-treatment.

Main Results:

  • Identified significant behavioral differences between patients with voice disorders and matched controls.
  • Observed significant differences in some patients before and after treatment.
  • Demonstrated the potential of symbolic mismatch analysis for differentiating vocal behaviors.

Conclusions:

  • The unsupervised data mining approach offers an objective method for diagnosing behavioral voice disorders.
  • This study is a foundational step towards a data-driven understanding of voice therapy's impact.
  • Ambulatory accelerometer data provides valuable insights into vocal misuse patterns.