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Classification of Voice Quality Using Neck-Surface Acceleration: Comparison With Glottal Flow and Radiated Sound.

Marcin Włodarczak1, Bogdan Ludusan2, Johan Sundberg3

  • 1Department of Linguistics, Stockholm University, Sweden.

Journal of Voice : Official Journal of the Voice Foundation
|August 26, 2022
PubMed
Summary
This summary is machine-generated.

Miniature accelerometers on the trachea and standard microphones show similar accuracy in classifying voice phonation types. These signals offer comparable performance to direct voice source analysis for voice quality assessment.

Keywords:
accelerometeraudiophonation type classificationvoice source

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Area of Science:

  • Speech Science
  • Biomedical Engineering
  • Acoustics

Background:

  • Classifying phonation type is crucial for understanding voice production and diagnosing voice disorders.
  • Traditional methods rely on analyzing the voice source or radiated sound, which can be influenced by vocal tract filtering.
  • Accelerometer signals from the tracheal wall offer a potential alternative, less affected by vocal tract characteristics.

Purpose of the Study:

  • To evaluate the effectiveness of features from tracheal accelerometers for phonation type classification.
  • To compare accelerometer-based features with those from inverse filtered voice source and radiated sound.
  • To identify which voice source aspects are discernible from accelerometer and microphone signals.

Main Methods:

  • Five trained singers produced the syllable [pæ:] in neutral, breathy, and pressed phonation types at preferred pitches.
  • Features were extracted from tracheal accelerometer signals, inverse filtered voice source, and radiated audio.
  • A random forest classifier was used to assess classification performance; feature correlations with voice source were analyzed.

Main Results:

  • Accelerometer and audio signals demonstrated comparable classification error rates for phonation type, similar to voice source analysis.
  • Phonation type variations consistently affected voice source, accelerometer, and audio signals across speakers.
  • Specific features like AQ, NAQ, L1L2, CQ (voice source), HRF, L1L2, CPPS (accelerometer/audio), and sound level (audio) showed systematic variations and were important for classification.

Conclusions:

  • Tracheal accelerometer signals can discriminate between phonation types with accuracy comparable to direct voice source measures.
  • Despite being less contaminated by vocal tract resonances, accelerometer signals did not outperform standard microphone recordings for this task.
  • Both accelerometer and audio signals provide valuable, complementary information for voice quality assessment.