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Linguistic Rhythm Model: Insights and Applications

Explore the adaptive model of linguistic rhythm by Sean McLennan, bridging theories of rhythm perception and speech segmentation. Discover how %V, ΔV, and ΔC influence segmentation based on rhythm types.

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Linguistic Rhythm Model: Insights and Applications

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  1. An Adaptive, Dynamical Model of Linguistic Rhythm Sean McLennan GLM 040312

  2. Underlying Intuitions • Somewhere between the signal and low level speech recognition, linguistic time is imposed upon real time. • Linguistic time is more relevant to speech recognition than real time. • Not all segments are created equal - certain points / intervals in the speech stream are more important for recognition than others. GLM - Sean McLennan - 040312

  3. What “Rhythm” Is and Is Not Rhythm - historically based primarily on the perception that different languages are temporally organized differently Three recognized rhythmic types: stress-timed (English), syllable-timed (French), and mora-timed (Japanese) Rhythm implies underlying isochrony which turns out to be absent (ex. Dauer, 1983) GLM - Sean McLennan - 040312

  4. Recent Views of Rhythm Ramus and colleagues: • examined three factors: %V ΔV ΔC • %V = proportion of vocalic intervals in the signal • ΔV = variation of length of vocalic intervals • ΔC = variation of length of consonantal intervals GLM - Sean McLennan - 040312

  5. Recent Views of Rhythm GLM - Sean McLennan - 040312

  6. Recent Views of Rhythm GLM - Sean McLennan - 040312

  7. Recent Views of Rhythm GLM - Sean McLennan - 040312

  8. Rhythm and Segmentation Cutler and Colleagues • study the question of how rhythm type impacts on the segmentation of words from the speech stream • implication being that a naïve listener (i.e. an infant) uses rhythm as a bootstrap for early stages of acquisition GLM - Sean McLennan - 040312

  9. Rhythm and Segmentation Syllable Effect: • French speakers spot “ba-” in balance faster than in balcon • French speakers spot “bal-” in balcon faster than in balance • rigorously reproduced, even on non-French words • “stubbornly” absent in English GLM - Sean McLennan - 040312

  10. Rhythm and Segmentation Stress Effect • Native English speakers find “mint” faster in mintesh than in mintayve • Native English speakers find “mint” slower in “mintayf” than in “mintef” and “thin” in thintayf or thintef. • In missegmentations - tend to insert before a stressed syllable (in vests) or delete before a weak syllable (bird in) GLM - Sean McLennan - 040312

  11. Rhythm and Segmentation Mora Effect • Native Japanese speakers find “ta-” in tanishi faster than in tanshi • Native Japanese speakers find “tan-” faster in tanshi than in tanishi. • Native Japanese speakers can find “uni” in gyanuni and gyaouni but fail to find it in gyabuni. • Native English speakers have no problem with the Japanese task • Native French speakers show the same cross-over effect with the Japanese task as in French and English GLM - Sean McLennan - 040312

  12. The Proposed Model • hopefully a bridge between Cutler et al and Ramus et al - why should %V ΔV ΔC impact on segmentation? • can a naïve adaptive model responsive to %V ΔV and ΔC produce behavior consistent with segmentation based on rhythm-type? GLM - Sean McLennan - 040312

  13. The Proposed Model • %V ΔV and ΔC need two points to be consistently tracked: vocalic onsets and offsets GLM - Sean McLennan - 040312

  14. The Proposed Model • Use these spikes to drive two adaptive oscillators (habituating neurons?) • Unlikely to entrain but will make predictions GLM - Sean McLennan - 040312

  15. The Proposed Model • The accuracy of prediction will be a measure of ΔC and ΔV • Difference in the period will be a measure of %V GLM - Sean McLennan - 040312

  16. The Proposed Model • ΔC ΔV and %V in turn determine the size of an “attentional window” • the attentional window is a metaphor for stimulus decay • The smaller ΔC and ΔV and closer %V is to 50%, the more periodic the rhythm, the narrower the window can be • The larger ΔC and ΔV and more divergent %V is from 50%, the less periodic the rhythm, the wider the window must be GLM - Sean McLennan - 040312

  17. The Proposed Model • Attentional window size (hopefully) would correlate with rhythm type and would predict different types of segmentation / recognition GLM - Sean McLennan - 040312

  18. The Proposed Model Predictions, questions, and other benefits: • consistent with the correlation between rhythmic type and consonant cluster complexity • consistent with ambisyllabicity • perhaps attractor states predict categorical differences • suggests manner in which to manipulate tasks to force effects • single language-independent mechanism GLM - Sean McLennan - 040312

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