differences in production account for differences in perception: the gradual learning algorithm...

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Differences in production account for differences in perception: The Gradual Learning Algorithm predicts language- specific cue weighting Paul Boersma, University of Amsterdam Paola Escudero, University of Reading Lisbon, February 2, 2001

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Page 1: Differences in production account for differences in perception: The Gradual Learning Algorithm predicts language-specific cue weighting Paul Boersma,

Differences in production account for differences in perception:

The Gradual Learning Algorithm predicts language-specific cue weighting

Paul Boersma, University of Amsterdam

Paola Escudero, University of Reading

Lisbon, February 2, 2001

Page 2: Differences in production account for differences in perception: The Gradual Learning Algorithm predicts language-specific cue weighting Paul Boersma,

Sound Contrasts

Sound contrasts and acoustic information An example of a sound contrast in English

What are the acoustic differences between the two?

Page 3: Differences in production account for differences in perception: The Gradual Learning Algorithm predicts language-specific cue weighting Paul Boersma,

Acoustic cues to “sheep” vs. “ship”

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Duration (ms)

Page 4: Differences in production account for differences in perception: The Gradual Learning Algorithm predicts language-specific cue weighting Paul Boersma,

Elspeth and Liz’s production environments

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Duration (ms)

E /I/

E /i/L /I/

L /i/

Page 5: Differences in production account for differences in perception: The Gradual Learning Algorithm predicts language-specific cue weighting Paul Boersma,

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

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Duration (ms)

?

E /I/

E /i/L /I/

L /i/

Page 6: Differences in production account for differences in perception: The Gradual Learning Algorithm predicts language-specific cue weighting Paul Boersma,

Why using the nearest production prototype in perception?

Why not perceiving it as an /e/ or an /o/?

Answer: “likelihood maximisation”, the most likely category

Functional principle: minimisation of perceptual confusion probability

Page 7: Differences in production account for differences in perception: The Gradual Learning Algorithm predicts language-specific cue weighting Paul Boersma,

How do Elspeth and Liz perceive the segments reliably?

 

 

 

 

 

 

 

 

 

 

 

 

 

 

[350 Hz,

80 ms]

350 Hz

not /I/

80 ms

not /i/

80 ms

not /I/

350 Hz

not /i/

/I/ *! *

/i/ * *

[350 Hz,

80 ms]

350 Hz

not /i/

80 ms

not /i/

80 ms

not /I/

350 Hz

not /I/

/I/ * *

/i/ *! *

Page 8: Differences in production account for differences in perception: The Gradual Learning Algorithm predicts language-specific cue weighting Paul Boersma,

How about baby Elspeth and baby Liz?

Little Elspeth and little Liz

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Page 9: Differences in production account for differences in perception: The Gradual Learning Algorithm predicts language-specific cue weighting Paul Boersma,

How about baby Elspeth and baby Liz?

Little Elspeth makes a mistake when categorising [350 Hz, 80 ms]

 

 

 

 

 

 

[350 Hz,

80 ms]

350 Hz

not /i/

80 ms

not /i/

80 ms

not /I/

350 Hz

not /I/

/I/ * *

/i/ *! *

Page 10: Differences in production account for differences in perception: The Gradual Learning Algorithm predicts language-specific cue weighting Paul Boersma,

Little Elspeth and little Liz have learned to perceive “sheep” and “ship” reliably”, they are adults

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Liz

Elspeth

Page 11: Differences in production account for differences in perception: The Gradual Learning Algorithm predicts language-specific cue weighting Paul Boersma,

Little Elspeth and little Liz have learned to perceive “sheep” and “ship” reliably”, they are adults

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Liz

Elspeth

Page 12: Differences in production account for differences in perception: The Gradual Learning Algorithm predicts language-specific cue weighting Paul Boersma,

The perception of real adult Elspeth and Liz

4.49 4.09

Elspeth

reliance 11%, weight 10%

[i]

[I]

[i ]

[I ]

5.2

3.71

Liz

reliance 32%, weight 30%

[i]

[I]

[i ]

[I ]