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Stop-Consonant Perception in 7.5-month-olds:
Evidence for gradient categories
Bob McMurray & Richard N. Aslin
Department of Brain and Cognitive SciencesUniversity of Rochester
Title Slide
With thanks to Julie Markant & Robbie Jacobs
Understanding spoken language requires that children learn a complex mapping…
Learning Language
What is the form of this mapping?
How do the demands of learning affect this representation?
Lexicon
All labs
Bob’s lab
NP
the lab
S
VP
produced
MeaningAcousticAcoustic LexiconLexicon
Language Understanding
Speech perception and word recognition require mapping…
Learning Speech
What representations mediate acoustics and lexical or sublexical units?
How does learning affect this representation?
AcousticAcoustic Sublexical Units
/b/
/la//a/
/l/ /p/
/ip/
Sublexical Units
/b/
/la//a/
/l/ /p/
/ip/
LexiconLexicon
Syntax, semantics,
pragmatics…
Speech Recognition
…continuous, variable perceptual input toa something discrete, categorical.
1) Acoustic mappings: Categorical and gradient perception in adults and infants.
2) Infant speech categories are graded representations of continuous detail.
3) Statistical learning models and sparse representations.
4) Conclusions and future directions.
Overview
Overview
What is the nature of the mapping between continuous perception and discrete categories?
How are these representations sensitive (or not) to within-category detail?
Categorization & Categorical Perception
Representation of Speech Detail
Empirical approach:• Use continuously variable stimuli.• Explore response using
Discrimination Identification (adults)Habituation (infants)
Categorical Perception 1
B
P
Subphonemic within-category variation in VOT is discarded in favor of a discrete symbol (phoneme).
• Sharp labeling of tokens on a continuum.
VOT
0
100
PB
% /p
/
ID (%/pa/)0
100
Discrim
ination
Discrimination
• Discrimination poor within a phonetic category.
Categorical Perception
Categorical Perception 2
Many tasks have demonstrated within-category sensitivity in adults...
Discrimination Task Variations Pisoni and Tash (1974) Pisoni & Lazarus (1974)Carney, Widin & Viemeister (1977)
Training Samuel (1977)Pisoni, Aslin, Perey & Hennessy (1982)
Goodness Ratings Miller (1997)Massaro & Cohen (1983)
BUT…
And lexical activation shows systematic sensitivity to subphonemic detail (McMurray, Tanenhaus & Aslin, 2002).
Infant Categorical Perception 1
Infants have shown a different pattern.
For 30 years, virtually all attempts to address this question have yielded categorical discrimination.
Categorical Perception in Infants
Exception: Miller & Eimas (1996).• Only at extreme VOTs.• Only when habituated to non- prototypical token.
GWB
Infant Categorical Perception 3
Nonetheless, infants possess abilities that would require within-category sensitivity. Su
ckin
g R
ate
(int
eres
t)
B BB B BB P P P P
Suck
ing
Rat
e (int
eres
t)
B BB B BB P P P P
• Infants can use allophonic differences at word boundaries for segmentation (Jusczyk, Hohne & Bauman, 1999; Hohne, & Jusczyk, 1994)
• Infants can learn phonetic categories from distributional statistics (Maye, Werker & Gerken, 2002).
Distributional Learning 2
Speech production causes clustering along contrastive phonetic dimensions.
Distributional Learning
E.g. Voicing / Voice Onset TimeB: VOT ~ 0P: VOT ~ 40
Result: Bimodal distribution
Within a categories, VOT is distributed Gaussian.
VOT0ms 40ms
• track frequencies of tokens at each value along a stimulus dimension.
VOT
freq
uenc
y
0ms 50ms
Distributional Learning 1
Distributional Learning
To statistically learn speech categories, infants must:
• This requires ability to track specific VOTs.
• Extract categories from the distribution.
+voice -voice
?Question 1
Prior examinations of speech-categories used:
• HabituationDiscrimination not ID.Possible selective adaptation.Possible attenuation of sensitivity.
• Synthetic speechNot ideal for infants.
• Single exemplar/continuumNot necessarily a category representation
Experiment 1: Reassess this issue with improved methods.
HTPP 1Misperception 3
Head-Turn Preference Procedure (Jusczyk & Aslin, 1995)
Infants exposed to a chunk of language:
• Words in running speech.
• Stream of continuous speech (ala statistical learning paradigm).
• Word list.
Head-Turn Preference Procedure
After exposure, memory for exposed items (or abstractions) is assessed by comparing listening time to consistent items with inconsistent items.
HTPP 2Misperception 3
Test trials start with all lights off.
HTPP 2Misperception 3
Center Light blinks.
HTPP 3Misperception 3
Brings infant’s attention to center.
HTPP 3Misperception 3
One of the side-lights blinks.
When infant looks at side-light……he hears a word
Beach… Beach… Beach…
HTPP 4Misperception 3
…as long as he keeps looking.
HTPP 5Misperception 3
Experiment 1 MethodsMisperception 3
Experiment 1
7.5 month old infants exposed to either 4 b-, or 4 p-words.
80 repetitions total.
Form a category of the exposed class of words.
PeachBeach
PailBail
PearBear
PalmBomb
Measure listening time on…
VOT closer to boundary
Competitors
Original words
Pear*Bear*
BearPear
PearBear
Experiment 1 StimuliMisperception 3
B* and P* were judged /b/ or /p/ at least 90% consistently by adult listeners.
B*: 97%P*: 96%
Stimuli constructed by cross-splicing naturally produced tokens of each end point.
B: M= 3.6 ms VOTP: M= 40.7 ms VOT
B*: M=11.9 ms VOTP*: M=30.2 ms VOT
Experiment 1 Familiarity vs.
NoveltyMisperception 3
Novelty/Familiarity preference varies across infants and experiments.
1221P
1636B
FamiliarityNoveltyWithin each group will we see evidence for gradiency?
Familiarity vs. Novelty
We’re only interested in the middle stimuli (b*, p*).
Infants were classified as novelty or familiarity preferring by performance on the endpoints.
Categorical
Experiment 1 Fam. vs. Nov. 2Misperception 3
Gradiency
What about in between?
After being exposed to bear… beach… bail… bomb…
Infants who show a novelty effect……will look longer for pear than bear.
Gradient
Bear*Bear Pear
Lis
teni
ng T
ime
4000
5000
6000
7000
8000
9000
10000
Target Target* Competitor
Lis
ten
ing
Tim
e (m
s)
Experiment 1 Results
Experiment 1 Results Nov
B
P
Exposed to:
Novelty infants (B: 36 P: 21)
Target vs. Target*:Competitor vs. Target*:
p<.001p=.017
Experiment 1 Results Fam
Familiarity infants (B: 16 P: 12)
Target vs. Target*:Competitor vs. Target*:
P=.003p=.012
4000
5000
6000
7000
8000
9000
10000
Target Target* Competitor
Lis
ten
ing
Tim
e (m
s) B
P
Exposed to:
Experiment 1 Results Planned PMisperception 3
Planned Comparisons
Infants exposed to /p/
NoveltyN=21
P P* B
.024*
.009**
P P* B
.024*
.009**
4000
5000
6000
7000
8000
9000
10000
Lis
ten
ing
Tim
e (m
s)
P* B4000
5000
6000
7000
8000
9000
.018*
.028*
.018*
P
Lis
ten
ing
Tim
e (m
s).028*
FamiliarityN=12
NoveltyN=36
<.001**>.1
<.001**>.2
4000
5000
6000
7000
8000
9000
10000
B B* P
Lis
ten
ing
Tim
e (m
s)
Experiment 1 Results Planned B
Misperception 3
Infants exposed to /b/
FamiliarityN=16
4000
5000
6000
7000
8000
9000
10000
B B* P
Lis
ten
ing
Tim
e (m
s).06
.15
Experiment 1 ConclusionsMisperception 3
7.5 month old infants show gradient sensitivity to subphonemic detail.
• Clear effect for /p/• Effect attenuated for /b/.
Experiment 1 Conclusions
Contrary to all previous work:
Experiment 1 Conclusions 2Misperception 3
Reduced effect for /b/… But:
Bear Pear
Lis
teni
ng T
ime
Bear*
Null Effect?
Bear Pear
Lis
teni
ng T
ime
Bear*
Expected Result?
Experiment 1 Conclusions 3Misperception 3
• Bear* Pear
Bear Pear
Lis
teni
ng T
ime
Bear*
Actual result.
• Category boundary lies between Bear & Bear*• Between (3ms and 11 ms).
• Will we see evidence for within-category sensitivity with a different range?
Experiment 2Misperception 3
Same design as experiment 1.
VOTs shifted away from hypothesized boundary (7 ms).
Train
40.7 ms.Palm Pear Peach Pail
3.6 ms.Bomb* Bear* Beach* Bale*
-9.7 ms.Bomb Bear Beach Bale
Test:
Bomb Bear Beach Bale -9.7 ms.
Experiment 2 Results FamMisperception 3
Experiment 2 Results
Familiarity infants (34 Infants)
4000
5000
6000
7000
8000
9000
B- B P
Lis
ten
ing
Tim
e (m
s)
=.05*
=.01**
Experiment 2 Results NovMisperception 3
Experiment 2 Results
Novelty infants (25 Infants)
=.02*
=.002**
4000
5000
6000
7000
8000
9000
B- B P
Lis
ten
ing
Tim
e (m
s)
Experiment 2 ConclusionsMisperception 3
Experiment 2 Conclusions
• Within-category sensitivity in /b/ as well as /p/.
VOT
Adult boundary
/b/ /p/
Cat
egor
y M
appi
ngS
tren
gth
Adult Categories
• Shifted category boundary in /b/: not consistent with adult boundary (or prior infant work). Why?
Experiment 2 Conclusions 2Misperception 3
/b/ results consistent with (at least) two mappings.
VOT
Adult boundary
/b/ /p/
Cat
egor
y M
appi
ngS
tren
gth
1) Shifted boundary
• Inconsistent with prior literature.
• Why would infants have this boundary?
Experiment 2 Conclusions 3Misperception 3
2) Sparse Categories/b/
VOT
Adult boundary
/p/
Cat
egor
y M
appi
ngS
tren
gth
unmappedspace
HTPP is a one-alternative task. Asks: B or not-B not: B or P
Sparse categories may in fact by a by-product of efficient statistical learning.
Model IntroMisperception 3
Distributional learning model/b/
VOT
Adult boundary
/p/
Cat
egor
y M
appi
ngSt
reng
th
unmappedspace/b/
VOT
Adult boundary
/p/
Cat
egor
y M
appi
ngSt
reng
th
unmappedspace
Computational Model
1) Model distribution of tokens asa mixture of gaussian distributions over phonetic dimension (e.g. VOT) .
2) After receiving an input, the Gaussian with the highest posterior probability is the “category”.
VOT
3) Each Gaussian has threeparameters:
Model Intro 2Misperception 3
Statistical Category Learning
1) Start with a set of randomly selected Gaussians.
2) After each input, adjust each parameter to find best description of the input.
3) Start with more Gaussians than necessarymodel doesn’t innately know how many
categories. -> for unneeded categories.
VOT VOT
Model Intro 3Misperception 3
Model Overgen Misperception 3
Overgeneralization • large • costly: lose phonetic distinctions…
Model UndergenMisperception 3
Undergeneralization• small • not as costly: maintain distinctiveness.
Model err on side of caution
To increase likelihood of successful learning:• err on the side of caution.• start with small
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 10 20 30 40 50 60
Starting
P(S
ucc
ess)
2 Category Model
3 Category Model
Model Sparseness
Sparseness coefficient: % of space not mapped to any category.
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0 2000 4000 6000 8000 10000 12000
Training Epochs
Avg
Sp
arsi
ty C
oeff
icie
nt Starting
VOT
.5-1
Unmapped space
Small
Model Sparseness
2
Sparseness coefficient: % of space not mapped to any category.
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0 2000 4000 6000 8000 10000 12000
Training Epochs
Avg
Sp
arsi
ty C
oeff
icie
nt
20-40
Starting
VOT
.5-1
Model Sparseness
3
Sparseness coefficient: % of space not mapped to any category.
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0 2000 4000 6000 8000 10000 12000
Training Epochs
Avg
Sp
arsi
ty C
oeff
icie
nt
12-17
3-11
Starting
VOT
.5-1
20-40
Model Conclusions
Small starting ’s lead to sparse category structure during infancy—much of phonetic space is unmapped.
Occasionally model leaves sparse regions at the end of learning.
1) Competition/Choice framework:• Additional competition or selection mechanisms
during processing allows categorization on the basis of incomplete information.
Model Conclusions
To avoid overgeneralization……better to start with small estimates for
Model Conclusions 2
• Similar properties in terms of starting and the resulting sparseness.
2) Non-parametric models
VOT
Categories• Competitive Hebbian Learning
(Rumelhart & Zipser, 1986).• Not constrained by a particular
equation—can fill space better.
Conclusions 3
Final Conclusions
Infants show graded response to within-category detail.
/b/-results suggest regions of unmapped phonetic space.
Statistical approach provides support for sparseness.• Given current learning theories, sparseness results
from optimal starting parameters.
Empirical test will require a two-alternative task.• AEM: train infants to make eye-movements in
response to stimulus identity.
Future Work
Future Work
• Infants make anticipatory eye-movements along predicted trajectory, in response to stimulus identity.
• Two alternatives allows us to distinguish between category boundary and unmapped space.
Last Word
Early speech categories emerge from an interplay of
• Exquisite sensitivity to graded detail in the signal.
• Long-term sensitivity to statistics of the signal.
• Early biases to optimize the learning problem.
-60 -40 -20 0 20 40 60 80
VOT
The last word