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Stochastic time models of syllable structure.

Jason A Shaw1, Adamantios I Gafos2

  • 1MARCS Institute, University of Western Sydney, Penrith, New South Wales, Australia; School of Humanities and Communication Arts, University of Western Sydney, Penrith, New South Wales, Australia.

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Summary
This summary is machine-generated.

This study models speech articulation dynamics using syllable structure, revealing its resilience to variability. The findings challenge previous phonetic heuristics and offer a robust method for analyzing speech patterns.

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Area of Science:

  • Linguistics
  • Phonology
  • Speech Science
  • Computational Linguistics

Background:

  • Generative linguistics and complex systems offer frameworks for understanding phonological structure.
  • Speech movement data from 3D electromagnetic articulography and X-ray microbeam provide insights into articulation dynamics.

Purpose of the Study:

  • To develop a modeling paradigm linking phonological structure (syllables) to speech movement data.
  • To investigate the role of syllable structure in speech articulation variability.
  • To evaluate the model's accuracy against experimental data from Arabic and English.

Main Methods:

  • Developed a modeling paradigm integrating phonology, stochastic methods, and complex systems.
  • Mapped syllabic organization to discrete coordination topologies to model temporal dynamics of speech articulation.
  • Simulated speech dynamics under different syllabic parses and compared with experimental articulography and X-ray microbeam data.

Main Results:

  • Model simulations successfully replicated key experimental findings on speech articulation.
  • The study identified conditions where phonetic heuristics for syllable structure are valid or fallible.
  • The modeling approach demonstrated the resilience of syllable structure to measurement, speaker, and contextual variability.

Conclusions:

  • Syllable structure is a key determinant of speech articulation dynamics, robust against various sources of variability.
  • The developed modeling paradigm offers a reliable method for diagnosing syllable structure from speech data.
  • Future extensions of the paradigm to acoustic data are promising for broader applications in speech analysis.