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

An Adaptive, Dynamical Model of Linguistic Rhythm

Sean McLennanGLM 040312

GLM - Sean McLennan - 040312

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.


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)


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








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



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



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)



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


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?


The Proposed Model

• %V ΔV and ΔC need two points to be consistently tracked: vocalic onsets and offsets


The Proposed Model

• Use these spikes to drive two adaptive oscillators (habituating neurons?)

• Unlikely to entrain but will make predictions


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


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


The Proposed Model

• Attentional window size (hopefully) would correlate with rhythm type and would predict different types of segmentation / recognition


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

Documents

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