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      • Phonological well-formedness – as measured by cross-linguistic markedness and within-language frequency – influences which structures are (mis)produced in errors. Recent advances in constraint-based generative phonological theories (Optimality Theory, Harmonic Grammar, and Maximum Entropy Grammars) may provide insight into these patterns.
      compass.onlinelibrary.wiley.com/doi/10.1111/j.1749-818X.2011.00282.x
  1. In large collections of speech errors, phonological patterns emerge. Speech errors are shaped by phonotactic constraints, markedness, frequency, and phonological representations of prosodic and segmental structure.

  2. Nov 15, 2019 · 2. What is the empirical basis for speech error patterns that support a role for phonological grammar? Take homes: • SFUSED: large database of speech errors with robust methods • SFUSED English: results suggest a weaker role for phonotactics in speech errors (favors competing explanation)

  3. To increase the linguistic diversity of relevant evidence, a large collection of speech errors was investigated in Cantonese, an under-studied language with unique phonological structures. In particular, the Cantonese data were examined for nine psycholinguistic effects commonly used as a lens on word-form encoding.

  4. We investigate phonological effects of six frequently observed speech error patterns through the simulation of these patterns in language data collected from Swedish, Norwegian, and English sources in order to glean how effects of the same error pattern vary across the three languages.

  5. In large collections of speech errors, phonological patterns emerge. Speech errors are shaped by phonotactic constraints, markedness, frequency, and phonological representations of prosodic and segmental structure.

  6. May 1, 2009 · It is shown that stochastic disruption to the computational mechanisms realising a Harmonic Grammar (HG) can account for the broad empirical patterns of speech errors and develop methods for linking particular HG proposals to speech-error distributions.

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  8. Oct 24, 2018 · phonological patterns typical of speech error collections (regularity, word-onset effect, syllable constituent effect). Result: given certain parameters (trained on frequent vocabulary, internal and external input), the model produces errors that are phonotactically regular about 96.3% of the time.