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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.


Duration of measured intervals in Arabic.Each box corresponds to 567 data points (collapsing over data reported in [13,63]). Left box: LE-A (left edge to anchor interval), middle box: CC-A (center to anchor interval), right box: RE-A (right edge to anchor interval). Intervals shown here were right-delimited by the CMax anchor.
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pone.0124714.g006: Duration of measured intervals in Arabic.Each box corresponds to 567 data points (collapsing over data reported in [13,63]). Left box: LE-A (left edge to anchor interval), middle box: CC-A (center to anchor interval), right box: RE-A (right edge to anchor interval). Intervals shown here were right-delimited by the CMax anchor.

Mentions: We now turn to model fitting for our first Moroccan Arabic corpus, which consists of 22 words: bal ‘to urinate’, dbal ‘to fade’, tab ‘to repent’, ktab ‘book’, lih ‘for him’, glih ‘to grill’, bati ‘to spend the night’, sbati ‘belt’, bula ‘urine’, sbula, ‘thorn’, bulha ‘her urine’, sbulha ‘her ear (of grain)’, ksbulha ‘they owned it for her’, dulha nonce kdulha nonce, bkdulha nonce, kulha ‘eat for her’, skulha nonce, mskulha ‘to hold for her’, lan ‘to become soft’, flan ‘someone’, kflan nonce. Fig 6 summarizes interval measures for this corpus. It shows the mean duration of LE-A, CC-A, and RE-A intervals for 567 data points drawn across the entire corpus. The main observation is that the variability of the RE-A interval is lower than the CC-A interval and the LE-A interval. For a complete description of the data including statistical analyses see [13,63].


Stochastic time models of syllable structure.

Shaw JA, Gafos AI - PLoS ONE (2015)

Duration of measured intervals in Arabic.Each box corresponds to 567 data points (collapsing over data reported in [13,63]). Left box: LE-A (left edge to anchor interval), middle box: CC-A (center to anchor interval), right box: RE-A (right edge to anchor interval). Intervals shown here were right-delimited by the CMax anchor.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0124714.g006: Duration of measured intervals in Arabic.Each box corresponds to 567 data points (collapsing over data reported in [13,63]). Left box: LE-A (left edge to anchor interval), middle box: CC-A (center to anchor interval), right box: RE-A (right edge to anchor interval). Intervals shown here were right-delimited by the CMax anchor.
Mentions: We now turn to model fitting for our first Moroccan Arabic corpus, which consists of 22 words: bal ‘to urinate’, dbal ‘to fade’, tab ‘to repent’, ktab ‘book’, lih ‘for him’, glih ‘to grill’, bati ‘to spend the night’, sbati ‘belt’, bula ‘urine’, sbula, ‘thorn’, bulha ‘her urine’, sbulha ‘her ear (of grain)’, ksbulha ‘they owned it for her’, dulha nonce kdulha nonce, bkdulha nonce, kulha ‘eat for her’, skulha nonce, mskulha ‘to hold for her’, lan ‘to become soft’, flan ‘someone’, kflan nonce. Fig 6 summarizes interval measures for this corpus. It shows the mean duration of LE-A, CC-A, and RE-A intervals for 567 data points drawn across the entire corpus. The main observation is that the variability of the RE-A interval is lower than the CC-A interval and the LE-A interval. For a complete description of the data including statistical analyses see [13,63].

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.