Phonetic convergence in shadowed speech: A comparison of perceptual and acoustic measures

Research output: Contribution to journalConference article

Abstract

Phonetic convergence is highly variable across studies, measures, and analyses. The current paper describes a study that examined multiple acoustic measures in concert with a perceptual measure of phonetic convergence. The study employed a shadowing task in which multiple talkers shadowed words from a set of models. Across different scales of analysis, the acoustic measures were highly variable, yielding inconsistent results. Perceptual assessment of phonetic convergence provided a measure that was more stable, reliable, and valid than any single acoustic attribute. Mixed-effects regression modeling assessed the relative contributions of each acoustic attribute to perceived phonetic convergence on a word-by-word basis. This study demonstrates the utility of an approach that combines acoustic and perceptual measures of phonetic convergence.

Original languageEnglish
Pages (from-to)530-534
Number of pages5
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
StatePublished - 1 Jan 2013
Event14th Annual Conference of the International Speech Communication Association, INTERSPEECH 2013 - Lyon, France
Duration: 25 Aug 201329 Aug 2013

Fingerprint

Speech analysis
Acoustics
Attribute
Mixed Effects
Shadowing
Inconsistent
Speech
Regression
Valid
Modeling
Demonstrate

Keywords

  • Phonetic convergence
  • Speech perception
  • Speech production

Cite this

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AB - Phonetic convergence is highly variable across studies, measures, and analyses. The current paper describes a study that examined multiple acoustic measures in concert with a perceptual measure of phonetic convergence. The study employed a shadowing task in which multiple talkers shadowed words from a set of models. Across different scales of analysis, the acoustic measures were highly variable, yielding inconsistent results. Perceptual assessment of phonetic convergence provided a measure that was more stable, reliable, and valid than any single acoustic attribute. Mixed-effects regression modeling assessed the relative contributions of each acoustic attribute to perceived phonetic convergence on a word-by-word basis. This study demonstrates the utility of an approach that combines acoustic and perceptual measures of phonetic convergence.

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