Occurrence Frequencies of Acoustic Patterns of Vocal Fry in American English Speakers

J Voice. 2016 Nov;30(6):759.e11-759.e20. doi: 10.1016/j.jvoice.2015.09.011. Epub 2015 Nov 5.

Abstract

Objective: The goal of this study was to analyze the occurrence frequencies of three individual acoustic patterns (A, B, C) and of vocal fry overall (A + B + C) as a function of gender, word position in the sentence (Not Last Word vs. Last Word), and sentence length (number of words in a sentence).

Study design: This is an experimental design.

Methods: Twenty-five male and 29 female American English (AE) speakers read the Grandfather Passage. The recordings were processed by a Matlab toolbox designed for the analysis and detection of creaky segments, automatically identified using the Kane-Drugman algorithm. The experiment produced subsamples of outcomes, three that reflect a single, discrete acoustic pattern (A, B, or C) and the fourth that reflects the occurrence frequency counts of Vocal Fry Overall without regard to any specific pattern. Zero-truncated Poisson regression analyses were conducted with Gender and Word Position as predictors and Sentence Length as a covariate.

Results: The results of the present study showed that the occurrence frequencies of the three acoustic patterns and vocal fry overall (A + B + C) are greatest at the end of sentences but are unaffected by sentence length. The findings also reveal that AE female speakers exhibit Pattern C significantly more frequently than Pattern B, and the converse holds for AE male speakers.

Conclusions: Future studies are needed to confirm such outcomes, assess the perceptual salience of these acoustic patterns, and determine the physiological correlates of these acoustic patterns. The findings have implications for the design of new excitation models of vocal fry.

Keywords: AE speakers; Acoustic patterns; Automated detection of vocal fry; Gender differences; Vocal fry.

Publication types

  • Comparative Study

MeSH terms

  • Acoustics*
  • Adolescent
  • Adult
  • Algorithms
  • Female
  • Humans
  • Male
  • Pattern Recognition, Automated
  • Phonation*
  • Phonetics*
  • Sex Factors
  • Signal Processing, Computer-Assisted*
  • Sound Spectrography
  • Speech Acoustics*
  • Speech Production Measurement / methods*
  • Time Factors
  • Voice Quality*
  • Young Adult