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Area of Science:

  • Linguistics
  • Speech Science
  • Computational Phonetics

Background:

  • Irregular pitch periods (IPPs) signify important nonmodal phonation types in speech.
  • Accurate identification of IPPs is crucial for linguistic, pragmatic, and clinical analysis.
  • Manual identification of IPPs is labor-intensive and requires specialized training.

Purpose of the Study:

  • To evaluate an algorithm for automatic detection of IPPs in American English conversational speech.
  • To determine a perceptually relevant threshold for creak probabilities in IPP detection.

Main Methods:

  • An algorithm developed for creaky voice analysis was applied to conversational speech recordings.
  • Frame-by-frame creak probabilities were compared against expert hand labels.
  • A threshold of approximately 0.02 was established for distinguishing IPPs.

Main Results:

  • The automatic detection algorithm demonstrated generally good agreement with hand-labeled IPPs.
  • The established threshold of 0.02 provides a basis for automated IPP identification.
  • This automated method offers a more efficient alternative to manual IPP analysis.

Conclusions:

  • Automatic detection of IPPs using creaky voice analysis is a promising approach.
  • Further research is needed to explore the influence of linguistic and prosodic contexts on IPP detection.
  • This technology can advance the study of nonmodal phonation in speech.