modelling the perceptual development of phonological contrasts with ot and gradual learning...

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Modelling the perceptual development of phonological contrasts with OT and Gradual Learning Algorithm Paola Escudero, University of Reading [email protected] Paul Boersma, University of Amsterdam [email protected] 25 th Penn Linguistics Colloquium March 3, 2001

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Modelling the perceptual development of phonological contrasts with OT and Gradual Learning Algorithm. Paola Escudero, University of Reading [email protected] Paul Boersma, University of Amsterdam [email protected] 25 th Penn Linguistics Colloquium March 3, 2001. - PowerPoint PPT Presentation

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Page 1: Modelling the perceptual development of phonological contrasts with  OT and Gradual Learning Algorithm

Modelling the perceptual developmentof phonological contrasts with

OT and Gradual Learning Algorithm

Paola Escudero, University of [email protected]

Paul Boersma, University of [email protected]

25th Penn Linguistics ColloquiumMarch 3, 2001

Page 2: Modelling the perceptual development of phonological contrasts with  OT and Gradual Learning Algorithm

Sound contrasts

Sound contrasts and acoustic information An example of a sound contrast

What are the acoustic differences between the two?

Page 3: Modelling the perceptual development of phonological contrasts with  OT and Gradual Learning Algorithm

Two acoustic cues to“ship” vs. “sheep”

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Duration (ms)

Page 4: Modelling the perceptual development of phonological contrasts with  OT and Gradual Learning Algorithm

Elspeth’s production environment(Scottish English)

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Duration (ms)

ship

sheep

lid

lead

filling

feeling

Snicker

sneaker

Page 5: Modelling the perceptual development of phonological contrasts with  OT and Gradual Learning Algorithm

Liz’s production environment(Southern English)

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Duration (ms)

ship

sheep

lid

lead

filling

feeling

Snicker sneaker

Page 6: Modelling the perceptual development of phonological contrasts with  OT and Gradual Learning Algorithm

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Duration (ms)

/I/

/i//I/

/i/

Elspeth’s and Liz’saverage production environments

Page 7: Modelling the perceptual development of phonological contrasts with  OT and Gradual Learning Algorithm

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Duration (ms)

?

/I/

/i//I/

/i/

Do Elspeth and Liz perceive [350 Hz, 80 ms] as “ship” or as “sheep”?

Liz Elspeth

Page 8: Modelling the perceptual development of phonological contrasts with  OT and Gradual Learning Algorithm

Why using the nearest production prototype in perception?

Answer: “likelihood maximisation”:choose the most likely produced category,given a certain F1 & duration

Functional principle: “minimise the probability of perceptual confusion”

Page 9: Modelling the perceptual development of phonological contrasts with  OT and Gradual Learning Algorithm

How Elspeth and Liz perceive the segments reliably

 

 

 

 

 

 

 

 

 

 

 

 

 

 

[350 Hz,80 ms]

350 Hznot /I/

80 msnot /i/

80 msnot /I/

350 Hznot /i/

/I/ *! * /i/ * *

[350 Hz,80 ms]

350 Hznot /i/

80 msnot /i/

80 msnot /I/

350 Hznot /I/

/I/ * */i/ *! *

Page 10: Modelling the perceptual development of phonological contrasts with  OT and Gradual Learning Algorithm

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Duration (ms)

Baby Elspeth or baby LizSpectral reliance 0.5%, duration reliance -0.3%

First stage of babies Elspeth and Liz

Page 11: Modelling the perceptual development of phonological contrasts with  OT and Gradual Learning Algorithm

How baby Elspeth learns Little Elspeth makes a mistake

when categorising [350 Hz, 80 ms] 

 

 

 

 

 

[350 Hz,80 ms]

350 Hznot /i/

80 msnot /i/

80 msnot /I/

350 Hznot /I/

/I/ * *

/i/ *! *

Page 12: Modelling the perceptual development of phonological contrasts with  OT and Gradual Learning Algorithm

How little Elspeth learns to perceive“ship” and “sheep” reliably (1)

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Duration (ms)

Elspeth after one monthSpectral reliance 80.9%, duration reliance 12.2%

Page 13: Modelling the perceptual development of phonological contrasts with  OT and Gradual Learning Algorithm

How little Elspeth learns to perceive“ship” and “sheep” reliably (2)

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Duration (ms)

Elspeth after 2 monthsSpectral reliance 96.9%, duration reliance 9.3%

Page 14: Modelling the perceptual development of phonological contrasts with  OT and Gradual Learning Algorithm

How little Elspeth learns to perceive“ship” and “sheep” reliably (3)

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Duration (ms)

Elspeth after 4 monthsSpectral reliance 96.4%, duration reliance 0.4%

Page 15: Modelling the perceptual development of phonological contrasts with  OT and Gradual Learning Algorithm

How little Elspeth learns to perceive“ship” and “sheep” reliably (4)

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Duration (ms)

Elspeth after 10 monthsSpectral reliance 98.6%, duration reliance 15.7%

Page 16: Modelling the perceptual development of phonological contrasts with  OT and Gradual Learning Algorithm

How little Elspeth learns to perceive“ship” and “sheep” reliably (5)

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Duration (ms)

Elspeth after 100 monthsSpectral reliance 97.8%, duration reliance 6.3%

Page 17: Modelling the perceptual development of phonological contrasts with  OT and Gradual Learning Algorithm

How little Elspeth learns to perceive“ship” and “sheep” reliably (6)

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Duration (ms)

Elspeth after 1000 monthsSpectral reliance 97.1%, duration reliance 6.5%

Page 18: Modelling the perceptual development of phonological contrasts with  OT and Gradual Learning Algorithm

How little Liz learns to perceive “ship” and “sheep” reliably (1)

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Duration (ms)

Liz after one monthSpectral reliance 34.6%, duration reliance 39.7%

Page 19: Modelling the perceptual development of phonological contrasts with  OT and Gradual Learning Algorithm

How little Liz learns to perceive “ship” and “sheep” reliably (2)

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Duration (ms)

Liz after 2 monthsSpectral reliance 37.1%, duration reliance 55.2%

Page 20: Modelling the perceptual development of phonological contrasts with  OT and Gradual Learning Algorithm

How little Liz learns to perceive “ship” and “sheep” reliably (3)

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Duration (ms)

Liz after 4 monthsSpectral reliance 53.5%, duration reliance 52.7%

Page 21: Modelling the perceptual development of phonological contrasts with  OT and Gradual Learning Algorithm

How little Liz learns to perceive “ship” and “sheep” reliably (4)

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Duration (ms)

Liz after 10 monthsSpectral reliance 46.3%, duration reliance 85.0%

Page 22: Modelling the perceptual development of phonological contrasts with  OT and Gradual Learning Algorithm

How little Liz learns to perceive “ship” and “sheep” reliably (5)

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Duration (ms)

Liz after 100 monthsSpectral reliance 51.9%, duration reliance 56.6%

Page 23: Modelling the perceptual development of phonological contrasts with  OT and Gradual Learning Algorithm

How little Liz learns to perceive “ship” and “sheep” reliably (6)

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Duration (ms)

Liz after 1000 monthsSpectral reliance 54.0%, duration reliance 51.4%

Page 24: Modelling the perceptual development of phonological contrasts with  OT and Gradual Learning Algorithm

The perception of real adult Elspeth and Liz

4.49 4.09

ScottishAverage

reliance 11%, weight 10%

[i]

[I]

[i:]

[I:]

5.23.71

SouthernAverage

reliance 32%, weight 30%

[i]

[I]

[i:]

[I:]

Page 25: Modelling the perceptual development of phonological contrasts with  OT and Gradual Learning Algorithm

What about L1-Spanish Isabel, who moves to Scotland and then

to Southern England?

Page 26: Modelling the perceptual development of phonological contrasts with  OT and Gradual Learning Algorithm

Isabel’s production environment (Spanish)

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Duration (ms)

/i/

/e/

Page 27: Modelling the perceptual development of phonological contrasts with  OT and Gradual Learning Algorithm

Isabel’s adult perception (Spanish)

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Isabel after 20 years in PeruSpectral reliance 99.9%, duration reliance 2.2%

Page 28: Modelling the perceptual development of phonological contrasts with  OT and Gradual Learning Algorithm

Isabel’s new production environment (Scottish English)

I FA

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Duration (ms)

/I/

/i/

Page 29: Modelling the perceptual development of phonological contrasts with  OT and Gradual Learning Algorithm

How Isabel learns to perceive“ship” and “sheep”

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Duration (ms)

Isabel after 10 years in EdinburghSpectral reliance 98.8%, duration reliance 4.4%

Page 30: Modelling the perceptual development of phonological contrasts with  OT and Gradual Learning Algorithm

Isabel’s new production environment (Southern English)

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Duration (ms)

/I/

/i/

Page 31: Modelling the perceptual development of phonological contrasts with  OT and Gradual Learning Algorithm

How Isabel learns to perceive“ship” and “sheep”

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Duration (ms)

Isabel after 10 years in LondonSpectral reliance 69.3%, duration reliance 44.5%

Page 32: Modelling the perceptual development of phonological contrasts with  OT and Gradual Learning Algorithm

Three types of real L2 categorisation

5.29

3.5abg

reliance 40%, weight 46%

[i]

[I]

[i:]

[I:] 3.4

3.29af

reliance 87%, weight 88%

[i]

[I]

[i:]

[I:]

4.4 4.8

mt

reliance -10%

[i]

[I]

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[I:]

4.38 4.67

mf

reliance -6%

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[I]

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[I:]

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2.5

1.92jg

reliance 37%, weight 33%

[i]

[I]

[i:]

[I:] 4.25

4.75ef

reliance 86%

[i]

[I]

[i:]

[I:]

Spectral reliance only Duration reliance onlyCue integration

Page 33: Modelling the perceptual development of phonological contrasts with  OT and Gradual Learning Algorithm

Summary For our perception model, we assumed:1) Perception is handled by an OT grammar 2) Its acquisition is handled by the GLA3) L2 learners start by copying their L1 grammar

With these assumptions, we can model:1) L1 Scottish and Southern English2) L2 Scottish and Southern English, partially

Page 34: Modelling the perceptual development of phonological contrasts with  OT and Gradual Learning Algorithm

Conclusion Cue reliance depends on cue reliability, or

a) Differences in the production environment account for differences in perception.

b) Changes in the production environment lead to changes in perception.

The functional principle underlying this production-perception dependence is “minimisation of perceptual confusion”.

This functional principle follows from our formal modelling of the perception grammar.

Page 35: Modelling the perceptual development of phonological contrasts with  OT and Gradual Learning Algorithm

Thank you for your attention!