an adaptive, dynamical model of linguistic rhythm sean mclennan proposal defense 040406

An Adaptive, Dynamical Model of Linguistic Rhythm

Sean McLennanProposal Defense 040406

Proposal Defense - Sean McLennan - 040406

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


Recent Views of Rhythm


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

• Showed that boundaries are imposed on the speech stream in a rhythm-class-appropriate manner


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 - Where

Finding salient points in the signal:


The Proposed Model - How Much

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



• Use these spikes to drive an adaptive oscillator• Unlikely to entrain but will make predictions



• The accuracy of prediction will be a measure of ΔC and ΔV

• Difference in the period will be a measure of %V



ΔV

Voc

Cons

ΔC


Proposed Model - How MuchProof of Concept - Periodic Signal


Proposed Model - How MuchProof of Concept - Aperiodic Signal



• Attentional window size (hopefully) would correlate with rhythm type and would predict different potential segmentation boundaries


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 particularly with respect to speaking rate and rhythmic priming

• single language-independent mechanism

an adaptive, dynamical model of linguistic rhythm sean mclennan proposal defense 040406

Documents

v slide

recent views of rhythm

signal v

proposed model predictions

aperiodic signal slide

v v c impact

appropriate manner slide

v difference