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Stochastic time models of syllable structure.

Shaw JA, Gafos AI - PLoS ONE (2015)

Bottom Line: Model simulations replicated several key experimental results, including the fallibility of past phonetic heuristics for syllable structure, and exposed the range of conditions under which such heuristics remain valid.More importantly, the modelling approach consistently diagnosed syllable structure proving resilient to multiple sources of variability in experimental data including measurement variability, speaker variability, and contextual variability.Prospects for extensions of our modelling paradigm to acoustic data are also discussed.

View Article: PubMed Central - PubMed

Affiliation: MARCS 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.

ABSTRACT
Drawing on phonology research within the generative linguistics tradition, stochastic methods, and notions from complex systems, we develop a modelling paradigm linking phonological structure, expressed in terms of syllables, to speech movement data acquired with 3D electromagnetic articulography and X-ray microbeam methods. The essential variable in the models is syllable structure. When mapped to discrete coordination topologies, syllabic organization imposes systematic patterns of variability on the temporal dynamics of speech articulation. We simulated these dynamics under different syllabic parses and evaluated simulations against experimental data from Arabic and English, two languages claimed to parse similar strings of segments into different syllabic structures. Model simulations replicated several key experimental results, including the fallibility of past phonetic heuristics for syllable structure, and exposed the range of conditions under which such heuristics remain valid. More importantly, the modelling approach consistently diagnosed syllable structure proving resilient to multiple sources of variability in experimental data including measurement variability, speaker variability, and contextual variability. Prospects for extensions of our modelling paradigm to acoustic data are also discussed.

No MeSH data available.


Model overview.Given any sequence of consonants and vowels, here “C C V X”, we exemplify our modelling paradigm by asking: is the sequence parsed in terms of syllables of the simplex or the complex onset type? To evaluate the two hypotheses, H1 vs. H2, the model projects coordination topologies from hypothesized syllable parses. The topology on the top/bottom embodies temporal relations of the simplex/complex onset parse. Absolute time (ms) predictions can be derived from these topologies, and their match to experimental data can be rigorously evaluated.
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pone.0124714.g003: Model overview.Given any sequence of consonants and vowels, here “C C V X”, we exemplify our modelling paradigm by asking: is the sequence parsed in terms of syllables of the simplex or the complex onset type? To evaluate the two hypotheses, H1 vs. H2, the model projects coordination topologies from hypothesized syllable parses. The topology on the top/bottom embodies temporal relations of the simplex/complex onset parse. Absolute time (ms) predictions can be derived from these topologies, and their match to experimental data can be rigorously evaluated.

Mentions: A schematic of the modelling paradigm is shown in Fig 3. Each syllabic parse can be mapped to a coordination topology ([18] page 316), reflecting the temporal relations underlying the segmental sequence. Two contrasting coordination topologies corresponding to a simplex onset parse (H1) and a complex onset parse (H2) of a segmental substring CCVX are shown in Fig 3. Mnemonics are ‘C’ for any consonant, ‘V’ for any vowel, and ‘X’ for any string over the C,V alphabet. These topologies specify timing relations between consonants and vowels, indicated by lines between the segments so related. Different topologies act as mutually exclusive independent variables, e.g. in the example of Fig 3, for any given CCV sequence, the parse in which both consonants are part of the onset, as per the English syllable structure, is pitted against the parse in which only the prevocalic C is included in a syllable with the V, as per the Arabic syllable structure. The task is to identify the topology accounting for the most variability in the data. For example, it is expected that for a CCV string in a language that does not admit complex onsets, the simplex onset topology would explain more variability than the complex onset topology.


Stochastic time models of syllable structure.

Shaw JA, Gafos AI - PLoS ONE (2015)

Model overview.Given any sequence of consonants and vowels, here “C C V X”, we exemplify our modelling paradigm by asking: is the sequence parsed in terms of syllables of the simplex or the complex onset type? To evaluate the two hypotheses, H1 vs. H2, the model projects coordination topologies from hypothesized syllable parses. The topology on the top/bottom embodies temporal relations of the simplex/complex onset parse. Absolute time (ms) predictions can be derived from these topologies, and their match to experimental data can be rigorously evaluated.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4440707&req=5

pone.0124714.g003: Model overview.Given any sequence of consonants and vowels, here “C C V X”, we exemplify our modelling paradigm by asking: is the sequence parsed in terms of syllables of the simplex or the complex onset type? To evaluate the two hypotheses, H1 vs. H2, the model projects coordination topologies from hypothesized syllable parses. The topology on the top/bottom embodies temporal relations of the simplex/complex onset parse. Absolute time (ms) predictions can be derived from these topologies, and their match to experimental data can be rigorously evaluated.
Mentions: A schematic of the modelling paradigm is shown in Fig 3. Each syllabic parse can be mapped to a coordination topology ([18] page 316), reflecting the temporal relations underlying the segmental sequence. Two contrasting coordination topologies corresponding to a simplex onset parse (H1) and a complex onset parse (H2) of a segmental substring CCVX are shown in Fig 3. Mnemonics are ‘C’ for any consonant, ‘V’ for any vowel, and ‘X’ for any string over the C,V alphabet. These topologies specify timing relations between consonants and vowels, indicated by lines between the segments so related. Different topologies act as mutually exclusive independent variables, e.g. in the example of Fig 3, for any given CCV sequence, the parse in which both consonants are part of the onset, as per the English syllable structure, is pitted against the parse in which only the prevocalic C is included in a syllable with the V, as per the Arabic syllable structure. The task is to identify the topology accounting for the most variability in the data. For example, it is expected that for a CCV string in a language that does not admit complex onsets, the simplex onset topology would explain more variability than the complex onset topology.

Bottom Line: Model simulations replicated several key experimental results, including the fallibility of past phonetic heuristics for syllable structure, and exposed the range of conditions under which such heuristics remain valid.More importantly, the modelling approach consistently diagnosed syllable structure proving resilient to multiple sources of variability in experimental data including measurement variability, speaker variability, and contextual variability.Prospects for extensions of our modelling paradigm to acoustic data are also discussed.

View Article: PubMed Central - PubMed

Affiliation: MARCS 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.

ABSTRACT
Drawing on phonology research within the generative linguistics tradition, stochastic methods, and notions from complex systems, we develop a modelling paradigm linking phonological structure, expressed in terms of syllables, to speech movement data acquired with 3D electromagnetic articulography and X-ray microbeam methods. The essential variable in the models is syllable structure. When mapped to discrete coordination topologies, syllabic organization imposes systematic patterns of variability on the temporal dynamics of speech articulation. We simulated these dynamics under different syllabic parses and evaluated simulations against experimental data from Arabic and English, two languages claimed to parse similar strings of segments into different syllabic structures. Model simulations replicated several key experimental results, including the fallibility of past phonetic heuristics for syllable structure, and exposed the range of conditions under which such heuristics remain valid. More importantly, the modelling approach consistently diagnosed syllable structure proving resilient to multiple sources of variability in experimental data including measurement variability, speaker variability, and contextual variability. Prospects for extensions of our modelling paradigm to acoustic data are also discussed.

No MeSH data available.