The influence of task engagement on phonetic convergence

Tifani Biro, Joseph C. Toscano, Navin Viswanathan

Research output: Contribution to journalReview articlepeer-review

2 Scopus citations

Abstract

Phonetic convergence is the process by which interlocutors’ speech becomes more similar to each other during an interaction. Talker factors like sex, race, and conversational role appear to influence convergence. Yet, findings vary in how these factors impact convergence and which acoustic features talkers converge on. There is also evidence that task differences may contribute to this variability. The present study investigated how task engagement influences phonetic convergence. Participant dyads worked together to complete a series of word-matching puzzles in either a highly engaging task (navigating a virtual world in the computer game Minecraft™) or a less engaging task (clicking on the words from a list). On a given block, one participant (the director) was given a list of words to tell the other participant (the matcher), who used the words to complete a series of puzzles. Thirty word-initial monosyllabic voicing minimal pairs served as stimuli, and we measured the degree to which phonetic features of voicing (VOT, vowel length, F1 onset, and f0 onset) changed over the course of the experiment. Convergence was inconsistent for VOT. However, we found that task engagement affected convergence for secondary voicing features (F1 onset and vowel length), such that convergence was only observed among interlocutors in the high engagement task for these acoustic dimensions. These findings suggest that the extent of phonetic convergence on certain acoustic features can be influenced by how engaged participants are with their task environment.

Original languageEnglish (US)
Pages (from-to)50-66
Number of pages17
JournalSpeech Communication
Volume138
DOIs
StatePublished - Mar 2022

All Science Journal Classification (ASJC) codes

  • Software
  • Modeling and Simulation
  • Communication
  • Language and Linguistics
  • Linguistics and Language
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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