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

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Presentation on theme: "An Adaptive, Dynamical Model of Linguistic Rhythm Sean McLennan GLM 040312."— Presentation transcript:

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

2 GLM - Sean McLennan 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.

3 GLM - Sean McLennan 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)

4 GLM - Sean McLennan 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

5 GLM - Sean McLennan Recent Views of Rhythm

6 GLM - Sean McLennan Recent Views of Rhythm

7 GLM - Sean McLennan Recent Views of Rhythm

8 GLM - Sean McLennan 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

9 GLM - Sean McLennan 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

10 GLM - Sean McLennan 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)

11 GLM - Sean McLennan 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

12 GLM - Sean McLennan 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?

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

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

15 GLM - Sean McLennan 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

16 GLM - Sean McLennan 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

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

18 GLM - Sean McLennan 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


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