International Child Phonology Conference
16 June 2011
New sound patterns are learned first
in frequently heard words
Mits Ota
University of Edinburgh
Sam Green
University College London
1
Phonology vs. words
Targetlike
production
/tw/
/pl/
/kr/
Age
2
Phonology vs. words
Targetlike
production
[pl]um
/pl/
[pl]ease
[pl]ay
[pl]ate
Age
3
Factors behind lexical variability
Age of word acquisition (AoWA) (Garlock et al., 2001)
Neighborhood density (Garlock et al., 2001; Munson & Swenson, 2005)
Lexical frequency (Gierut, 2001; Stoel-Gammon, 2010)
  In production
  In the input



4
Previous work: Results
Input frequency effects on production accuracy?
Population
Yes
No
TD; 5-7 years
Leonard & Ritterman
(1971)
Moore, Burke, & Adams,
(1976); Garlock, Walley, &
Metsala (2001)
PD; 3-7 years
Gierut, Morrisette, &
Champion (1999); Gierut &
Storkel (2002); Morrisette
& Gierut (2002)
TD; 1-3 years
Ota (2006)
5
Sosa (2008)
Previous work: Sources of mixed results
Population
Sound patterns analyzed
Frequency estimates
Confounds
  Input frequency ~ production frequency (≈ sampling),
AoWA, neighborhood density, word length
Method
  Elicited production of selected words
  Longitudinal spontaneous production data





6
Current study
Population : 1-4 year old TD children
Sound patterns analyzed : Word-initial consonant clusters
Frequency estimates : Based on mother’s speech
Confounds : Controlled in regression analysis
  Input frequency ~ production frequency (≈ sampling),
AoWA, neighborhood density, word length
Method
  Elicited production of selected words
  Longitudinal spontaneous production data





Survival analysis
7
Sampling problem in corpus analysis
20 25 30 35 40 45
plus
Pluto
Accuracy
?→
1.0
0.8
0.6
0.4
0.2
0.0
please
pleasure
Production accuracy of /pl/- in a
spontaneous speech corpus
plenty
plow
plum
plate
play
playdoh
plan
plane
planet
←?
←?
plant
1.0
0.8
0.6
0.4
0.2
0.0
plastic
?→
1.0
0.8
0.6
0.4
0.2
0.0
place
20 25 30 35 40 45
plain
20 25 30 35 40 45
Age (months)
8
20 25 30 35 40 45
1.0
0.8
0.6
0.4
0.2
0.0
Survival analysis
start
Proportion of
people alive
4
3
1
2
Patient ID
5
6
end
0
20
40
60
80
Time since start of study
= Death (observed)
= Death (unobserved)
 = Last confirmation
9
100
Time
A survival curve for heart
transplant patients
(critical event = death)
Cox regression
Proportion of
people alive
non-smokers
smokers
Time
10
Covariates
Smoking**
Gender
Age***
No. of heart attacks**
Survival analysis of cluster acquisition
start
Proportion
not acquired
4
3
1
2
WordID
Patient
5
6
end
0
20
40
60
80
100
Time since start of study
= Accuracy ≥ 80% (observed)
= Accuracy ≥ 80% (unobserved)
 = Last obs of accuracy < 80%
11
Time
A ‘survival curve’ for words
with an initial cluster
(critical event = acquisition)
Cox regression on cluster acquisition
Proportion not
acquired
high frequency
Low frequency
Time
12
Covariates
Input frequency?
AoWA?
Word length?
Neighborhood density?
Cluster type?
Specific research questions


All other factors being equal, does input frequency increase
the proportion of words in which an initial consonant cluster
is acquired (= 80%+ accuracy) before 4?
Does input frequency equally affect different types of wordinitial clusters (e.g., /pl/, /sw/, /skr/)?
  Production of early-acquired prosodic forms in Japanese is
less affected by input frequency (Ota, 2006)
13
Data

Providence Corpus (Demuth, Culbertson & Alter, 2006)
  Longitudinal
  Phonetically transcribed
  Includes maternal speech
Child
14
Age
No of
sessions
Tokens of
words with
cluster
Types of
words with
cluster
Lily
1;3-4;0
80
5,209
481
Naima
0;11-4;0
88
7,140
521
Violet
1;2-4;0
54
1,536
271
Age of cluster acquisition



Age of cluster acquisition for each word = First 3-month bin
with production accuracy above 80%
Nontargetlike production
  Deletion: [pe] ‘play’
  Epenthesis: [pəle] ‘play’
  Substitution: [pwe] ‘play’
Targetlike production
  Everything else
15
Predictor variables (covariates)





Input frequency: Summed token count of each lexical item in
mother’s speech (log)
Production frequency: Mean monthly token count of each
lexical item in child’s speech (log)
Age of word acquisition: Month of first production attempt
Neighborhood density: Number of neighbors (log)
Word size: Number of phonemes
16
Predictor variables (covariates)


Cluster size: CC (e.g., /st/, /pl/) vs. CCC (e.g., /str/, /spl/)
Cluster type:
  C(C)w: /tw/ (twinkle), /skw/ (squash)
  C(C)j: /mj/ (music), /skj/ (skewer)
  C(C)r: /kr/ (cry), /spr/ (spring)
  C(C)l: /kl/ (clean), /spl/ (splash)
  SN: /sn/ (snow), /sm/ (small)
  SP: /st/ (star), /sk/ (skip)
17
Frequency effects on survival curves
0.8
0.0
0.2
0.4
0.6
0.8
0.6
0.4
0.2
0.0
Proportion of words with cluster not acquired
1.0
Below-median frequency
1.0
Above-median frequency
15
20
25
30
35
Months
18
40
45
15
20
25
30
35
Months
40
45
Correlations between covariates
35
45
-3
-1 0
1
2
4
6
8
10
12 0.0
1.0
2.0
3.0
0 1 2 3 4 5 6
15 25 35 45
25
First
AoWA
1
15 25 35 45
15
Production
frequency
12
-3
-1
LogMeanAttempts
3.0
4
6
8
NPhon
Word
size
0.0
6
4
2
Input
frequency
LogMotFreq
0
= Lily
= Naima
= Violet
1.5
N’hood
Log.Nb
density
0 1 2 3 4 5 6
19
Cox regression:
Words learned before 2
C(C)r and C(C)l slower
AoWA
Cluster type
-18%***
Input
frequency
20
+16%*
List 1
(AoWA ≤
2;0)
Input
frequency x
Cluster type
Frequency effect weaker
in C(C)r and C(C)j
Cox regression:
Words learned between 2 and 3
C(C)l, C(C)j and SN faster;
C(C)r slower
Cluster type
Frequency effect weaker in C(C)l,
C(C)j, SN and C(C)r
Input
frequency x
Cluster type
AoWA
Word length
-7%**
Input
frequency
21
+11%***
-8%**
List 2
2;0 < AoWA
≤ 3;0
-15%***
N’hood
density
Finding 1
Production of initial clusters is mastered first in
frequently heard words.

Why input frequency matters?
  Exposure updates lexical representation
  Misrepresentation as a source of production inaccuracy
(Macken, 1992)
  Improvement of representation and exposure (Schwartz &
Terrell, 1983; Swingley, 2007)
  Perception-production mismatch places pressure to
overcome output restrictions (Coetzee & Pater, 2009)
22
Finding 2
Lexical input frequency effects are weaker in fastacquired clusters – except C(C)r.

The problem with C(C)r is /r/, not the cluster.





Lily (3;1.0)
Naima (3;10.10)
Violet (3;7.22)
[wɛd] ‘red’
[wuf] ‘roof’
[waɪʔ] ‘right’
The rest is consistent with Ota’s (2006) observation: ‘Easy’
sound pattern = less frequency effects
Generalization of learning from words to sound patterns?
23
Finding 3
For words learned between 2 and 3, acquisition of initial
clusters is also faster in short words and in sparse
neighborhoods.


Implications for the emergence of lexical neighborhoods in
children (Charles-Luce & Luce, 1990, Coady & Aslin, 2003, Dollaghan,
1994)
Counter evidence to the Lexical Restructuring Model (Metsala
& Walley, 1998)?
24
Implications



Frequency: Sounds vs. words
Frequency: Input vs. output
Development: Phonology vs. words
25