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Learnability of English diphthongs: One dynamic target vs. two static targets

  • Anqi Xu*
  • , Daniel R. van Niekerk
  • , Branislav Gerazov
  • , Paul Konstantin Krug
  • , Santitham Prom-on
  • , Peter Birkholz
  • , Yi Xu
  • *Corresponding author for this work
  • Harbin Institute of Technology Shenzhen
  • University College London
  • SS Cyril and Methodius University in Skopje
  • Technische Universität Dresden
  • King Mongkut's University of Technology Thonburi

Research output: Contribution to journalArticlepeer-review

Abstract

As vowels with intrinsic movements, diphthongs are among the most elusive sounds of speech. Previous research has characterized diphthongs as a combination of two vowels, a vowel followed by a formant transition, or a constant rate of formant change. These accounts are based on acoustic patterns, perceptual cues, and either acoustic or articulatory synthesis, but no consensus has been reached. In this study, we explore the nature of diphthongs by exploring how they can be acquired through vocal learning. The acquisition is simulated by a three-dimensional (3D) vocal tract model with built-in target approximation dynamics, which can learn articulatory targets of phonetic categories under the guidance of a speech recognizer. The simulation attempts to learn to articulate diphthong-embedded monosyllabic English words with either a single dynamic target or two static targets, and the learned synthetic words were presented to native listeners for identification. The results showed that diphthongs learned with dynamic targets were consistently more intelligible across variable durations than those learned with two static targets, with only the exception of /aɪ/. From the perspective of learnability, therefore, English diphthongs are likely unitary vowels with dynamic targets.

Original languageEnglish
Article number103225
JournalSpeech Communication
Volume170
DOIs
StatePublished - May 2025
Externally publishedYes

Keywords

  • 3D vocal tract model
  • American English
  • Computational simulation
  • Diphthongs
  • Vocal learning

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