PERSPECTIVE
Do Infants Really Learn Phonetic Categories?
Naomi H. Feldman1*
, Sharon Goldwater2*, Emmanuel Dupoux3,4, and Thomas Schatz1
1Department of Linguistics and UMIACS, University of Maryland, College Park, MARYLAND, Etats-Unis
2School of Informatics, University of Edinburgh, United Kingdom
3Cognitive Machine Learning (ENS – EHESS – PSL Research University – CNRS – INRIA), Paris, France
4Facebook A.I. Research, Paris, France
*These authors contributed equally to this work.
un accès ouvert
journal
Mots clés: language acquisition, speech perception, computational modeling, representation
learning
ABSTRAIT
Early changes in infants’ ability to perceive native and nonnative speech sound contrasts are
typically attributed to their developing knowledge of phonetic categories. We critically
examine this hypothesis and argue that there is little direct evidence of category knowledge in
infancy. We then propose an alternative account in which infants’ perception changes
because they are learning a perceptual space that is appropriate to represent speech, without
yet carving up that space into phonetic categories. If correct, this new account has substantial
implications for understanding early language development.
INTRODUCTION
Infants’ perception of speech becomes specialized for the native language even before their
first birthday. Discrimination of native contrasts improves, and discrimination of nonnative
contrasts declines (Kuhl et al., 2006; Werker & Tees, 1984). These changes are often assumed
to reflect the development of adultlike perceptual patterns, and more specifically of adultlike
phonetic category representations: linguistically relevant categories that are phoneme-length
and correspond roughly to the consonants and vowels of a language (Best, 1994; Kuhl et al.,
1992; Werker et al., 2007; Zevin, 2012).1 These assumptions have been motivated by the
close ties observed in adults between native language phonetic categories and language-
specific patterns of discrimination along phonetically relevant dimensions, as shown schemat-
ically in Figure 1 (Liberman et al., 1957).
If early changes in discrimination result from early knowledge of phonetic categories—
discrete units, with or without explicit labels, that roughly correspond to linguistically rele-
vant sounds like [r] (as in rock) et [je] (as in lock)—then infants must learn these categories
by their first birthday. The categories would then drive changes to their perceptual space
(Figure 2a). Cependant, phonetic categories are difficult to learn from the speech infants hear
(Antetomaso et al., 2017; Bion et al., 2013), raising doubts about the feasibility of early
1 Contextual variants of a phoneme are generally treated as different categories, with phonetic categories cor-
responding roughly to allophones (Dillon et al., 2013; Werker & Curtin, 2005; but see Pegg & Werker, 1997).
Citation: Feldman, N. H., Goldwater, S.,
Dupoux, E., & Schatz, T. (2021). Do
Infants Really Learn Phonetic
Categories? Open Mind: Discoveries
in Cognitive Science, 5, 113–131.
https://doi.org/10.1162/opmi_a_00046
EST CE QUE JE:
https://doi.org/10.1162/opmi_a_00046
Reçu: 31 Janvier 2020
Accepté: 6 Août 2021
Intérêts concurrents:
The authors declare no conflict
of interest.
Corresponding Authors:
Naomi H. Feldman
nhf@umd.edu
Sharon Goldwater
sgwater@inf.ed.ac.uk
droits d'auteur: © 2021
Massachusetts Institute of Technology
Publié sous Creative Commons
Attribution 4.0 International
(CC PAR 4.0) Licence
La presse du MIT
je
D
o
w
n
o
un
d
e
d
F
r
o
m
h
t
t
p
:
/
/
d
je
r
e
c
t
.
m
je
t
.
/
e
d
toi
o
p
m
je
/
je
un
r
t
je
c
e
–
p
d
F
/
d
o
je
/
je
.
/
/
1
0
1
1
6
2
o
p
m
_
un
_
0
0
0
4
6
1
9
6
9
1
5
7
o
p
m
_
un
_
0
0
0
4
6
p
d
.
/
je
F
b
oui
g
toi
e
s
t
t
o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3
Do Infants Learn Phonetic Categories?
Feldman et al.
Chiffre 1. Hypothetical identification and discrimination functions in two-alternative forced
choice tasks.
phonetic category learning. Early phonetic category learning has been questioned before
( Jusczyk, 1992), yet only a few concrete alternative accounts of infants’ changes in discrim-
ination have been proposed (Guenther & Gjaja, 1996; Herrmann et al., 1995; Matusevych
et coll., 2020; Schatz et al., 2021).
Here we critically examine the evidence for phonetic category learning in infancy and highlight
recent developments in speech technology which, nous nous disputons, can inspire an alternative account of
early perceptual learning where phonetic categories are not involved. Under this account, early
changes in discrimination are caused by a learning process that—without recourse to phonetic
categories—transforms the acoustic similarity space, changing the perceptual distances between
je
D
o
w
n
o
un
d
e
d
F
r
o
m
h
t
t
p
:
/
/
d
je
r
e
c
t
.
m
je
t
.
/
e
d
toi
o
p
m
je
/
je
un
r
t
je
c
e
–
p
d
F
/
d
o
je
/
je
/
/
.
1
0
1
1
6
2
o
p
m
_
un
_
0
0
0
4
6
1
9
6
9
1
5
7
o
p
m
_
un
_
0
0
0
4
6
p
d
.
/
je
F
b
oui
g
toi
e
s
t
t
o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3
Chiffre 2. Phonetic category learning vs. perceptual space learning. (un) Under standard phonetic category learning theories, infants identify
categories early. Par conséquent, perception becomes warped along phonetically relevant dimensions (Dimension 1) and discrimination decreases
along phonetically irrelevant dimensions (Dimension 2). (b) An alternative theory is that learners’ perceptual space undergoes substantial
changes before phonetic categories are learned. In this simplistic example, perceptual learning collapses the dimension of lower variance,
decreasing discrimination along Dimension 2. As described later, we believe perceptual space learning actually involves more complex
transformations.
OPEN MIND: Discoveries in Cognitive Science
114
Do Infants Learn Phonetic Categories?
Feldman et al.
sounds (Figure 2b). Phonetic categories are learned later, or more gradually, by carving up this
learned space. We refer to the earlier phase of learning as perceptual space learning2 and discuss
several algorithms that might be used to implement such learning, including learning without
any discrete units, or with units that do not correspond meaningfully to phones. Changes in
discrimination driven by knowledge of phonetic categories could in principle also be consid-
ered a type of perceptual space learning, but here we restrict the term to mean learning with-
out phonetic categories. We do not argue conclusively against the early phonetic category
learning hypothesis; instead, we argue that perceptual space learning, which has thus far
received little attention in the language acquisition literature, should be seriously considered
as a plausible alternative theory of what causes infants’ perceptual changes.
Attributing infants’ perceptual changes to perceptual space learning would have major im-
plications for theories of language acquisition. Phonetic category learning has conventionally
been thought to occur before ( Werker et al., 2009) or alongside (Swingley, 2009) word learning,
enabling word forms to be composed of sequences of phones from the earliest stages. This hy-
pothesized trajectory makes phonetic category learning a difficult problem because it cannot
draw on extensive knowledge of word meanings, which would provide information about
which sounds in a language are meaningfully different (Trubetzkoy, 1939). Cependant, if phonetic
category learning occurs later in childhood, it could draw on a broad array of word meanings
and minimal pairs, making it an easier problem (McMurray et al., 2018). Perceptual space learn-
ing would also have broad implications for other areas of language acquisition, such as under-
standing when and how infants notice that words are mispronounced (Curtin et al., 2009;
Fennell & Werker, 2003; Rost & McMurray, 2009; Stager & Werker, 1997), studying whether
infant-directed speech is optimized for phonetic learning (Cristia & Seidl, 2014; Eaves et al.,
2016; Kuhl et al., 1997; McMurray et al., 2013), or understanding the challenges of adult second
language learning (Flege & Hillenbrand, 1986; Francis & Nusbaum, 2002; Lipski et al., 2012;
Underbakke et al., 1988; Ylinen et al., 2009). Plus généralement, it would radically change our view
of what children know at the beginning of their second year, a period when they rapidly acquire
aspects of language related to grammar and meaning.
CHILDREN’S PERCEPTUAL LEARNING
je
D
o
w
n
o
un
d
e
d
F
r
o
m
h
t
t
p
:
/
/
d
je
r
e
c
t
.
m
je
t
.
/
e
d
toi
o
p
m
je
/
je
un
r
t
je
c
e
–
p
d
F
/
d
o
je
/
je
.
/
/
1
0
1
1
6
2
o
p
m
_
un
_
0
0
0
4
6
1
9
6
9
1
5
7
o
p
m
_
un
_
0
0
0
4
6
p
d
.
/
je
The primary evidence for phonetic category learning in infancy comes from experiments that
measure infants’ discrimination of native and nonnative sound contrasts. The discrimination
tasks do not inherently require category knowledge (Box 1), but they do reveal changes in
discrimination that are suggestive of category learning (as articulated by Zevin, 2012).
Discrimination of nonnative speech contrasts generally declines during the first year of life:
by 10–12 months for consonants and by 6–8 months for vowels (Anderson et al., 2003;
Best & McRoberts, 2003; Best et al., 1995; Bosch & Sebastián-Gallés, 2003; Burns et coll.,
2007; Kuhl et al., 1992; Segal et al., 2016; Tsuji & Cristia, 2014; Werker & Lalonde, 1988;
Werker & Tees, 1984). During the same time period, discrimination of native contrasts gener-
ally improves (Burns et coll., 2007; Kuhl et al., 2006; Narayan et al., 2010; Tsao et coll., 2006).
Although there are exceptions to this pattern (Best et al., 1988; L. Liu & Kager, 2014, 2016;
Mattock & Burnham, 2006; Mazuka et al., 2014; Mugitani et al., 2009; Polka & Bohn, 1996;
Polka et al., 2001; Sundara et al., 2006; Yeung et al., 2013), it is clear that infants’ perception
becomes more native-like as they are exposed to their native language.
F
b
oui
g
toi
e
s
t
t
o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3
2 In machine learning the usual term is unsupervised representation learning, but we want to avoid confusion
caused by the broader meaning of representation in cognitive science.
OPEN MIND: Discoveries in Cognitive Science
115
Do Infants Learn Phonetic Categories?
Feldman et al.
Box 1. DO INFANT DISCRIMINATION TASKS REQUIRE CATEGORY KNOWLEDGE?
Most tests of infant speech perception have used one of two paradigms. In a habituation exper-
iment, infants experience repeated trials in which they hear a habituation stimulus—exemplars from
one phonetic category—while viewing a visual display. Once their looking time to habituation trials
falls below a threshold, discrimination is measured as the extent to which they look longer at change
trials (with exemplars from another category) than at same trials (with exemplars from the habituated
catégorie). Infants need to be able to discriminate a contrast in order to show different looking behav-
ior toward change trials and same trials. Cependant, infants can succeed at this task without knowing
phonetic categories, as long as they perceive the stimuli on change trials to be acoustically anom-
alous, relative to the habituation trials. Similar considerations hold for the oddball paradigm used by
Hochmann and Papeo (2014).
The other paradigm that is frequently used to measure infant speech perception is the condi-
tioned head turn (CHT) procedure, in which infants face an experimenter who is playing with toys
and hear a background stimulus from a loudspeaker on the side of the room. On change trials, le
stimulus changes to an exemplar from the other phonetic category, and they can look toward the
loudspeaker and see toys light up and start to move. On same trials, when the category does not
changement, looking toward the loudspeaker does not yield any visual reward. After an initial condi-
tioning phase, discrimination is assessed by measuring head turns on change trials, relative to same
trials. As in habituation experiments, infants need to be able to discriminate a contrast in order to
show different looking behavior toward change trials and same trials. Cependant, because this par-
adigm involves a decision of whether to perform a head turn, it resembles identification tasks in
some ways. Particularly striking are studies showing that when trained on a phonetic contrast, dans-
fants can generalize to novel speakers during test in a CHT paradigm (Kuhl, 1979, 1983). Ce
seems to suggest that infants already know that phonetic differences, but not speaker differences,
signal a category distinction.
Cependant, it is possible that the categorical patterns of generalization reflect learning that has
occurred during the experiment. The visual reinforcements that infants see during a CHT exper-
iment provide a reward signal that could engage reinforcement learning mechanisms, which ap-
pear to be particularly successful in driving auditory perceptual learning in adults (Lim et al.,
2019; Lim & Holt, 2011; Tricomi et al., 2006). In line with this, Kuhl (1979) notes that the infants
initially make head turns toward stimuli that vary from the background stimulus along irrelevant
dimensions, such as speaker or pitch, but that this tendency lessens over the course of the exper-
iment. She hypothesizes that learning has occurred during the experiment and suggests that
the infant demonstrates a proclivity to try to discover a criterial attribute which separates
the two categories. The infant, in effect, displays a tendency to be a “natural sorter," et
is attracted to a dimension which makes a set of multidimensional auditory stimuli fit
into easily recognized perceptual groupings. (p. 1674)
Autrement dit, Kuhl hypothesizes that it is the functional equivalence of different exemplars with
respect to the visual reinforcement in the CHT paradigm that supports learning of new cue weights.
Given that this learning could occur within the experiment itself, the categorical head-turn behavior
that infants exhibit within this paradigm does not necessarily support the strong hypothesis that they
come into the lab with well-formed phonetic categories (see Apfelbaum & McMurray, 2011, for a
similar argument). Si, and at what age, children use the same strategy to learn phonetic cat-
egories in more naturalistic settings remains an open question.
OPEN MIND: Discoveries in Cognitive Science
116
je
D
o
w
n
o
un
d
e
d
F
r
o
m
h
t
t
p
:
/
/
d
je
r
e
c
t
.
m
je
t
.
/
e
d
toi
o
p
m
je
/
je
un
r
t
je
c
e
–
p
d
F
/
d
o
je
/
je
/
.
/
1
0
1
1
6
2
o
p
m
_
un
_
0
0
0
4
6
1
9
6
9
1
5
7
o
p
m
_
un
_
0
0
0
4
6
p
d
/
.
je
F
b
oui
g
toi
e
s
t
t
o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3
Do Infants Learn Phonetic Categories?
Feldman et al.
A category-based account of these perceptual changes would entail that learners group
stimuli into discrete units that correspond roughly to the phones of a language. As shown in
Figure 2a, the categories would then drive changes in the perceptual space (Bonnasse-Gahot
& Nadal, 2008; Kuhl, 1979). Cependant, there are reasons to question whether categories are
the driving force behind infants’ perceptual changes. Box 2 distinguishes three perceptual ef-
fects that are often associated with category knowledge. If all three are direct results of cate-
gory knowledge, then they should develop in tandem, as categories are learned. Given the
substantial evidence that discrimination of nonnative contrasts declines sharply relative to na-
tive contrasts during infants’ first year (Effect 3), one might also expect to find sharpening cat-
egory boundaries (Effect 1) or sharpening discrimination peaks along phonetically relevant
dimensions (Effect 2) in young infants. Yet there is little evidence that these effects develop
during the same time period.
Box 2. PERCEPTUAL EFFECTS ASSOCIATED WITH CATEGORIES
Three types of perceptual effects are typically assumed to arise from category knowledge. While
there is substantial evidence that the first two are closely tied to knowledge of categories, or at least
distinct clusters of sounds, we argue that the third effect is more general, and need not reflect such
connaissance.
Effect 1 is a sharp category boundary in identification tasks (Liberman et al., 1957; Chiffre 1).
Performing an identification task requires category knowledge, given the use of category labels in
the task. Cependant, changes in steepness of the category boundary during learning could arise either
from changes in category knowledge, or from children’s improving ability to perform an identifica-
tion task. These two possibilities can be disambiguated through a phenomenon known as cue
weighting, which refers to the relative steepness of the identification curve across different dimen-
sions. Changes in cue weighting have been tied to category learning across many studies (Francis
et coll., 2000; Francis et al., 2008; Francis & Nusbaum, 2002; Holt & Loto, 2006; Idemaru & Holt,
2011, 2014; Lehet & Holt, 2017; R.. Liu & Holt, 2015; Lequel & Sundara, 2019; Ylinen et al., 2009),
and cue weights are also key to many models of categorization (Kruschke, 1992; Love et al., 2004;
Nosofsky, 1986; Toscano & McMurray, 2010), suggesting that this effect is closely tied to category
connaissance.
Effect 2 is a discrimination peak near the category boundary (Liberman et al., 1957; Chiffre 1).
While some models do attribute peaks in discrimination near the category boundary to category
connaissance (Feldman et al., 2009; Kuhl, 1993; Lacerda, 1995), other models have suggested that this
effect may only require distinct clusters in the distribution of sounds in the acoustic space (like the
distributions in the third panel in Figure 2a) even if the clusters are not recognized as discrete units
(Guenther & Gjaja, 1996; Herrmann et al., 1995; Shi et al., 2010). De plus, categories with high
variability (Figure 2a, second panel) may not yield a distinctive discrimination peak (Kronrod et al.,
2016). Ainsi, we take the discrimination peak to index how tightly clustered the distribution of sounds
is in listeners’ perceptual space. Whether well-separated clusters of sounds constitute perceptual
categories is a matter of some debate; to avoid overloading terminology, we simply refer to these
as clusters of sounds in a perceptual space.
Effect 3 is listeners’ differential ability to discriminate sounds along different dimensions. For ex-
ample, English listeners discriminating instances of [r] et [je] are more sensitive to differences in the
third formant than to differences in the second formant, whereas Japanese listeners have roughly
equal sensitivity to both dimensions (Iverson et al., 2003). Listeners can retain sensitivity to cues even
when they stop using those cues to categorize sounds (Lehet & Holt, 2020), so changes in sensitivity
OPEN MIND: Discoveries in Cognitive Science
117
je
D
o
w
n
o
un
d
e
d
F
r
o
m
h
t
t
p
:
/
/
d
je
r
e
c
t
.
m
je
t
.
/
e
d
toi
o
p
m
je
/
je
un
r
t
je
c
e
–
p
d
F
/
d
o
je
/
je
/
/
.
1
0
1
1
6
2
o
p
m
_
un
_
0
0
0
4
6
1
9
6
9
1
5
7
o
p
m
_
un
_
0
0
0
4
6
p
d
.
/
je
F
b
oui
g
toi
e
s
t
t
o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3
Do Infants Learn Phonetic Categories?
Feldman et al.
in discrimination tasks are not necessarily the same thing as changes in cue weighting. En théorie, it is
possible to lose or gain the ability to discriminate along certain dimensions even without representing
well-separated clusters of sounds in a perceptual space (Figure 2b; Chiffre 3; Chiffre 4); that is the
possibility we explore in this article.
The scope of this last effect merits consideration, because although discrimination is typically
assumed to be better along phonetically relevant dimensions than along phonetically irrelevant di-
mensions (cf. Goldstone, 1994), there are exceptions to this generalization (Best et al., 1988).
De plus, even if there were no exceptions, predicting exactly which contrasts are difficult to dis-
criminate requires knowing the dimensions of listeners’ perceptual space. The second formant in
tokens of [je] ou [r] may be a different perceptual dimension than the second formant in vowels, pour
instance. For the purposes of this article, we take the primary signature of Effect 2 to be a peak in
discrimination near a category boundary. Absent evidence of the development of such a peak, nous
tentatively assume that any changes in discrimination could instead be instances of Effect 3.
Identification tasks are challenging to carry out with infants, but the few studies that have
directly measured English-learning infants’ categorization have found extremely shallow iden-
tification boundaries (Burnham, 1986; Burnham et al., 1991). Boundaries become steeper—as
measured through aggregated data and individual participants’ identification functions—
entre 3 et 7 années, with differences even between 6- or 7-year-olds and adults in some
cases (Burnham, 1986; Burnham et al., 1991; Chen et al., 2017; Hazan & Barrett, 2000;
Krause, 1982; Kuijpers, 1996; McMurray et al., 2018; Ohde & Haley, 1997; Simon &
Fourcin, 1978; Zlatin & Koenigsknecht, 1975). These changes could be partly due to chil-
dren’s improving ability to perform identification tasks, but task difficulty is not the only factor.
Across much of the range between 3-year-olds and adults, the increase in category boundary
steepness depends on the category being tested (Slawinski & Fitzgerald, 1998) and on the spe-
cific phonetic dimensions along which those categories are tested (Greenlee, 1980; Hazan &
Barrett, 2000; Nittrouer, 1992; Nittrouer & Miller, 1997; Nittrouer & Studdert-Kennedy, 1987;
Ohde et al., 1995; Ohde & Haley, 1997), indicating that children are reweighting different
dimensions as cues to category membership. These differential changes in category boundary
steepness strongly suggest that at least some category learning occurs later in childhood.
Discrimination peaks along phonetically relevant dimensions sharpen in tandem with the
changes in category boundary steepness later in childhood (Chen et al., 2017; Medina et al.,
2010), whereas in infants, evidence for the development of discrimination peaks is mixed.
Chiffre 3. Example illustrating how different perceptual space learning methods could lead to different perceived distances between the
same original points. Ici, both methods map points from a two-dimensional space to a one-dimensional line. The mapping is shown ex-
plicitly for only four points; distances along the line correspond to perceptual distances in the learned space. (un) In the linear mapping, le
brown stars are mapped to the same location, so the distinction between these points is lost, whereas the red squares remain distinct. (b) In the
nonlinear mapping, the opposite holds.
OPEN MIND: Discoveries in Cognitive Science
118
je
D
o
w
n
o
un
d
e
d
F
r
o
m
h
t
t
p
:
/
/
d
je
r
e
c
t
.
m
je
t
.
/
e
d
toi
o
p
m
je
/
je
un
r
t
je
c
e
–
p
d
F
/
d
o
je
/
je
/
.
/
1
0
1
1
6
2
o
p
m
_
un
_
0
0
0
4
6
1
9
6
9
1
5
7
o
p
m
_
un
_
0
0
0
4
6
p
d
/
.
je
F
b
oui
g
toi
e
s
t
t
o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3
Do Infants Learn Phonetic Categories?
Feldman et al.
⇒
Chiffre 4. Perceptual space learning can make category learning easier. The marker shapes/colors represent ground truth category labels,
which are unknown to the learner; the dotted line highlights the transformation. The decision boundary is simpler after transforming the space.
Newborn and 6-month-old English and Swedish learners show cross-linguistic differences in
vowel perception for [je] et [oui] (Kuhl et al., 1992; Moon et al., 2013), and English-learning
6-month-olds’ discrimination is worse near a prototypical [je] than near a nonprototypical [je],
similar to adults (Grieser & Kuhl, 1989; Kuhl, 1991). These studies are suggestive, but do not
provide direct evidence that between-category discrimination peaks are developing in infancy.
In consonants, there are cross-linguistic differences in infants’ voice onset time ( VOT) discrim-
ination (Eilers et al., 1979; Streeter, 1976), with a clear peak in discrimination near the phonetic
category boundary in English-learning 1- and 4-month-old infants (Eimas et al., 1971). Cependant,
a meta-analysis of infant studies with English learners did not find evidence that the VOT dis-
crimination peak sharpens over the first year of life (Galle & McMurray, 2014). De plus, le
discrimination peak is also present in nonhuman animals (Kuhl, 1981; Kuhl & Miller, 1975; Kuhl
& Padden, 1982), suggesting that it arises from an auditory discontinuity. Whether auditory dis-
continuities constitute knowledge of categories, and how they relate to subsequent perceptual
learning, is less clear (Chládková & Paillereau, 2020). One study did find that French-learning
infants’ VOT discrimination changes between 4 et 8 months in the direction that would be
expected if they were learning phonetic categories (Hoonhorst et al., 2009), providing some
evidence of a developing discrimination peak. Dans l'ensemble, cependant, there is little convincing ev-
idence that peaks in discrimination along phonetically relevant dimensions sharpen substan-
tially during infants’ first year.
The literature thus suggests that different perceptual changes occur at different ages. Infants’
discrimination changes substantially during the first year (Effect 3), but changes that are diag-
nostic of category learning (Effect 1) and of increasing perceptual separation between clusters
of sounds (Effect 2) are most clearly documented later in childhood. Existing accounts never-
theless attribute both infant and childhood perceptual changes to category learning (Burnham,
1986; Zevin, 2012). We question this interpretation for two reasons. D'abord, as we argue in the
next section, general changes in discrimination are compatible with various perceptual space
learning algorithms that do not require phonetic categories at all. Deuxième, for phonetic cate-
gories to be the cause of those drastic early perceptual changes, one must either posit well-
developed categories (in which case the missing evidence of Effect 2 is puzzling), or suppose
that noisy, poorly developed categories can drive a drastic reshaping of the perceptual space
to yield Effect 3, even though those same category representations are too noisy to yield dis-
crimination peaks along phonetically relevant dimensions (Effect 2).
For these reasons, we believe it is time for the field to consider the possibility that infants’
perceptual changes primarily reflect a perceptual space learning process. Early perceptual
development would look more like Figure 2b, or a more sophisticated variant (discussed
following). Learning phonetic categories to carve up this perceptual space could then extend
OPEN MIND: Discoveries in Cognitive Science
119
je
D
o
w
n
o
un
d
e
d
F
r
o
m
h
t
t
p
:
/
/
d
je
r
e
c
t
.
m
je
t
.
/
e
d
toi
o
p
m
je
/
je
un
r
t
je
c
e
–
p
d
F
/
d
o
je
/
je
/
.
/
1
0
1
1
6
2
o
p
m
_
un
_
0
0
0
4
6
1
9
6
9
1
5
7
o
p
m
_
un
_
0
0
0
4
6
p
d
.
/
je
F
b
oui
g
toi
e
s
t
t
o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3
Do Infants Learn Phonetic Categories?
Feldman et al.
well into childhood and even adolescence. Although there is, as yet, little empirical evidence
to distinguish this hypothesis from the early phonetic category learning hypothesis, the latter
makes stronger assumptions about the nature of early representations that have yet to be
clearly validated.
COMPUTATIONAL APPROACHES TO PERCEPTUAL SPACE LEARNING
Although cognitive scientists have proposed a handful of perceptual space learning models for
speech (Gauthier et al., 2007; Guenther & Gjaja, 1996; Herrmann et al., 1995; Nixon &
Tomaschek, 2021; Westermann & Reck Miranda, 2004), perceptual space learning is more
actively studied in the machine learning community, where it is well-known that modified
representations of input features can be learned without access to, and without necessarily
resulting in, categorical knowledge. This type of learning has been used in many domains,
including vision and speech (Chung et al., 2019; Erhan et al., 2010; Kamper et al., 2015;
Ranzato et al., 2007; Schneider et al., 2019; van den Oord et al., 2018; Yu et al., 2010),
and there is even a series of recent speech technology challenge tasks devoted to the topic
(Dunbar et al., 2017, 2019; Versteegh et al., 2015).
Perceptual space learning is popular in machine learning because it can improve a system’s
ability to learn from the signal: for example for speech, spectral information, or even wave-
forms. In contrast, cognitive models often use more abstract features (such as formants) as in-
put. Cependant, starting from abstract features skips over a critical part of the learning process,
wherein infants must learn which of the many dimensions of raw speech are relevant to pro-
cessing their native language. We argue that this aspect of learning, which most cognitive
models do not consider at all, could explain many of the perceptual changes seen in young
infants.
To illustrate, consider a well-known method for perceptual space learning: principal com-
ponent analysis (APC). PCA reduces the dimensionality of data in order to learn a more com-
pact representation that still preserves the most important information. Par exemple, dans le
speech domain each input data point might represent a short (10 ms) slice of speech using
a vector where each dimension represents the value of some acoustic measure such as spectral
energy. Some of these dimensions may vary independently, while others may be highly cor-
related or simply record random noise—thus, most of the information can be represented using
a smaller number of dimensions. PCA identifies the orthogonal dimensions of greatest varia-
tion in the original data, rotates these to align with the axes of the vector space, and discards
dimensions with low variation. C'est, it learns a representation that is optimized to capture
the greatest amount of variance in the data.
The transformation learned by PCA is linear, since it simply rotates the axes of the space
before collapsing some dimensions. Cependant, many perceptual space learning methods are
more powerful, in that they learn a nonlinear transformation, warping the original space in
potentially arbitrary ways (Chiffre 3).3 The result is that points that were close together in the
input space may end up far apart in the learned space or vice versa. Donc, if discrimina-
tion depends on distance in some perceptual space (Shepard, 1987), perceptual space learning
could lead to changes in discrimination.
Although perceptual space learning is not directly optimized for categorization, it could
nevertheless help with later category learning by factoring out irrelevant features or warping
3 Although both of the illustrated methods reduce dimensionality, perceptual space learning can also maintain
or even increase dimensions; the key property is that it changes the shape of the input space.
OPEN MIND: Discoveries in Cognitive Science
120
je
D
o
w
n
o
un
d
e
d
F
r
o
m
h
t
t
p
:
/
/
d
je
r
e
c
t
.
m
je
t
.
/
e
d
toi
o
p
m
je
/
je
un
r
t
je
c
e
–
p
d
F
/
d
o
je
/
je
/
.
/
1
0
1
1
6
2
o
p
m
_
un
_
0
0
0
4
6
1
9
6
9
1
5
7
o
p
m
_
un
_
0
0
0
4
6
p
d
/
.
je
F
b
oui
g
toi
e
s
t
t
o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3
Do Infants Learn Phonetic Categories?
Feldman et al.
the space in a way that makes the category structure more obvious (Chiffre 4). This effect has
been demonstrated both in cognitive models of auditory learning (Gauthier et al., 2007; Roark
et coll., 2020) and in machine learning models, where “pretraining” a system’s perceptual space
on a generic unsupervised task (such as predicting the next input in a sequence) can improve
performance on a variety of downstream tasks (such as question answering or phone classifi-
cation) (Chung et al., 2019; Devlin et al., 2019; Erhan et al., 2010; Peters et al., 2018;
Schneider et al., 2019). While it is theoretically possible that systems pretrained on speech
could be implicitly learning phonetic categories, evidence from models that do learn quan-
tized representations (latent categories) suggests otherwise: the learned units are typically
far more granular than phonetic categories, and often cannot even be well-characterized as
sub-phones or subsets of phonetic categories (Baevski et al., 2020; Baevski, Schneider, & Auli,
2019; Chorowski et al., 2019; Hsu et al., 2021; Schatz et al., 2021).
These recent successes in machine learning have led to a proliferation of new work on
perceptual space learning algorithms. Ainsi, cognitive scientists should be considering not just
whether perceptual space learning could explain infants’ early perceptual development, mais
more specifically which algorithms might provide good models for infant learning. These
algorithms differ in the source of the learning signal and the cognitive plausibility and
domain-specificity of the mechanism. Par exemple, self-organizing maps (Kohonen, 1989,
2001) are an early method for nonlinear dimensionality reduction, based on competitive learn-
ing. More popular in the speech community are autoencoder neural networks (Chorowski
et coll., 2019; van Niekerk et al., 2020), which can be viewed as a domain-general learning
mechanism inspired by memory encoding: they learn to encode each input into an internal
representation that allows the original input to be reconstructed as closely as possible. Other
recent algorithms aim to predict missing or upcoming stretches of speech, with the learning
signal coming from prediction errors—another cognitively plausible domain-general mecha-
nism (Baevski, Auli, & Mohamed, 2019; Baevski et al., 2020; Baevski, Schneider, & Auli,
2019; Chung et al., 2019; Hsu et al., 2021).
There have also been recent proposals for more domain-specific perceptual space learning
methods that rely on a noisy top-down signal provided by knowledge of some word-like units
(Kamper et al., 2015; Renshaw et al., 2015; Riad et al., 2018; Thiollière et al., 2015). These
units can be found by searching for stretches of speech that form similar pairs or clusters, avec-
out any knowledge of phones ( Jansen & Van Durme, 2011; McInnes & Goldwater, 2011; Parc
& Verre, 2008; Räsänen & Blandon, 2020). Assuming that the clusters represent different in-
stances of the same word, the learner can then adjust its current representation of the low-level
speech features to make these instances even closer together in perceptual space. Preliminary
evidence suggests that models using this mechanism can learn representations that demon-
strate some of the effects seen in infants (Matusevych et al., 2020). At a high level, this is es-
sentially the mechanism proposed by Jusczyk (1992), and—unlike the other methods
described above—it does use a form of categorical knowledge (word categories) to guide
learning. Whereas we argue in the next section that phonetic categories are difficult to learn
due to high acoustic overlap, word-like units are likely to have fewer near acoustic neighbors
than phones (Swingley, 2009), which could make them easier for infants to discover in natu-
ralistic speech (cf. Jusczyk & Aslin, 1995; Jusczyk et al., 1999).
REVISITING PHONETIC CATEGORY LEARNING
Learners eventually develop sharp identification boundaries and discrimination peaks, provid-
ing evidence of well-separated categories (Box 2). Under a phonetic category learning account
OPEN MIND: Discoveries in Cognitive Science
121
je
D
o
w
n
o
un
d
e
d
F
r
o
m
h
t
t
p
:
/
/
d
je
r
e
c
t
.
m
je
t
.
/
e
d
toi
o
p
m
je
/
je
un
r
t
je
c
e
–
p
d
F
/
d
o
je
/
je
/
/
.
1
0
1
1
6
2
o
p
m
_
un
_
0
0
0
4
6
1
9
6
9
1
5
7
o
p
m
_
un
_
0
0
0
4
6
p
d
/
.
je
F
b
oui
g
toi
e
s
t
t
o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3
Do Infants Learn Phonetic Categories?
Feldman et al.
of infants’ perceptual changes, much of the category learning process happens in infancy.
Under a perceptual space learning account, category learning might occur later or more grad-
ually, and even if it begins in infancy, it is not the primary driver of infants’ perceptual changes.
Either way, there must be a mechanism for learning phonetic categories.
Distributional learning (Maye et al., 2002) has emerged as a leading hypothesis for a mech-
anism that could operate in infancy. Infants discriminate stimuli better after hearing a bimodal
distribution—with two distinct clusters of sounds—along the relevant phonetic dimension than
after hearing a unimodal distribution (Cristia, 2011; Maye et al., 2002; Maye et al., 2008;
Wanrooij et al., 2014; Yoshida et al., 2010; see Cristia, 2018, for a meta-analysis). This ability
to track acoustic distributions of sounds could support category learning if phonetic categories
corresponded to well-separated clusters of sounds.
Cependant, while some contrasts in laboratory speech are well-separated acoustically (Lisker
& Abramson, 1964), categories overlap substantially in naturalistic speech, as in the second
panel of Figure 2a (Antetomaso et al., 2017; Bard & Anderson, 1982; Bion et al., 2013;
Hitczenko et al., 2020; Pollack & Pickett, 1963; Swingley, 2019).4 Most models that have
tested the feasibility of distributional learning for identifying phonetic categories have simpli-
fied the learning problem, Par exemple, by using artificial data with low variability (McMurray
et coll., 2009; Pajak et al., 2013; Vallabha et al., 2007), focusing only on subsets of the catego-
ries infants would need to acquire (Adriaans & Swingley, 2017; de Boer & Kuhl, 2003;
Gauthier et al., 2007), or limiting the training data to a single speaker (Miyazawa et al.,
2010; Miyazawa et al., 2011). Similar models that were tested on more realistic datasets
showed much worse performance at learning phonetic categories (Adriaans & Swingley,
2012; Jones et al., 2012; Schatz et al., 2021). Donc, the distributional sensitivity that in-
fants exhibit in simplified laboratory settings may not be sufficient to learn phonetic categories
in naturalistic settings. This may still be true even after perceptual space learning (as in the
second panels of Figure 2b and Figure 4).
Aside from distributional information, phonetic category learners can draw on additional
sources of information, such as word forms or meanings (Swingley, 2009). Infants recognize
word forms in fluent speech (Bortfeld et al., 2005; Jusczyk & Aslin, 1995; Jusczyk et al., 1999)
and know some word meanings (Bergelson & Swingley, 2012); both can affect infants’ dis-
crimination in laboratory settings (Feldman, Myers, et coll., 2013; Yeung & Werker, 2009).
Cependant, unsupervised phonetic category learning models that use contextual information
have again done better when trained in idealized settings than in more naturalistic settings
(Antetomaso et al., 2017; Feldman et al., 2013; Frank et al., 2014; C.-Y. Lee et al., 2015).
These differences between naturalistic and idealized settings make category-based
accounts of infants’ perceptual changes less parsimonious than previously believed. Quand
categories are heavily overlapping along some dimensions, as in the second panel of
Figure 2a, separating them—even imperfectly, as in the third panel of Figure 2a—requires find-
ing better dimensions for representing the sounds in the underlying perceptual space. Un tel
transformation is similar to perceptual space learning, but is driven by category knowledge.
Ainsi, both the category-based account and the perceptual space learning account require the
same two learning processes. What is at stake is the interdependence and relative timing of
those processes. If phonetic category learning is as difficult as the above evidence suggests, it
might be more feasible for older children, who can draw on more knowledge of higher level
4 Although the degree of overlap depends on the specific dimensions measured, we know of no language-
universal set of dimensions that reliably yields well-separated phonetic categories (see also Chládková &
Paillereau, 2020).
OPEN MIND: Discoveries in Cognitive Science
122
je
D
o
w
n
o
un
d
e
d
F
r
o
m
h
t
t
p
:
/
/
d
je
r
e
c
t
.
m
je
t
.
/
e
d
toi
o
p
m
je
/
je
un
r
t
je
c
e
–
p
d
F
/
d
o
je
/
je
/
/
.
1
0
1
1
6
2
o
p
m
_
un
_
0
0
0
4
6
1
9
6
9
1
5
7
o
p
m
_
un
_
0
0
0
4
6
p
d
.
/
je
F
b
oui
g
toi
e
s
t
t
o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3
Do Infants Learn Phonetic Categories?
Feldman et al.
linguistic structure (McMurray et al., 2018) and benefit from using a learned perceptual space
with fewer irrelevant dimensions.
EMPIRICAL EVIDENCE FOR PERCEPTUAL SPACE LEARNING
There is not yet any direct evidence for a perceptual space learning process in infants.
Cependant, evidence from adults lends plausibility to such an account. After hearing nonspeech
stimuli in which two auditory dimensions are perfectly correlated, listeners can discriminate
between stimuli that follow the same correlation as in training, but not those that violate the
correlation (Stilp et al., 2010; Stilp & Kluender, 2012), suggesting that correlations among
dimensions can drive auditory perceptual space learning. The integration of perceptual dimen-
sions for perceiving speech is not always determined by experience (Kingston et al., 2008; S.
Lee & Katz, 2016), but several studies have suggested that an experience-based perceptual
space learning process could play a role (Holt et al., 2001; Nearey, 1997; Schertz et al.,
2020) and could interact in nontrivial ways with subsequent learning of cue weights (Roark
et coll., 2020; Roark & Holt, 2019; Scharinger et al., 2013).
Adults are additionally sensitive to temporal structure within perceptual dimensions. Their
attention to dimensions in visual perception, such as color or shape, is affected by the tempo-
ral statistics within each dimension (Zhao et al., 2013)—that is, conditional probabilities,
which infants are sensitive to in auditory perception (Saffran et al., 1996). This attentional
benefit may well have an analogue in the auditory domain, given that auditory exposure to
temporal regularities elicits increased MEG amplitude in auditory cortex relative to random
sequences (Barascud et al., 2016). Although there is not yet evidence linking this attentional
benefit of temporal structure to infants’ early perceptual changes, such a strategy could poten-
tially be effective at identifying informative perceptual dimensions, because language has con-
siderable internal structure.
THE WAY FORWARD
To begin testing which type of theory best accounts for early perceptual development in
speech, it is important to take seriously the complexity of speech produced in naturalistic
environnements. Naturalistic speech varies along many more acoustic dimensions than are typ-
ically manipulated in stimuli for speech perception experiments, or represented in phonetic
learning models, and several studies have already shown that considering the variability of
naturalistic speech can change our understanding of perceptual development (Antetomaso
et coll., 2017; Bion et al., 2013; Hitczenko et al., 2020). Methods for working with speech in
naturalistic settings have been developed in the context of engineering applications, and nat-
uralistic speech corpora now exist in numerous languages. By adapting these tools (par exemple.,
Räsänen, 2011; Räsänen & Rasilo, 2015; Schatz, 2016; Schatz et al., 2013, 2021; Schatz
et coll., 2018; Schatz & Feldman, 2018), cognitive scientists can begin investigating the role
of perceptual space learning in explaining how infants’ perception of speech becomes special-
ized for their native language.5
Thus far, we know of only a handful of models that have been evaluated against infant
behavioral data after training on natural continuous speech. Schatz et al. (2021) trained a
bottom-up distributional learner—specifically, a Dirichlet process Gaussian mixture model—
on low-level spectral representations of speech from Japanese or English. The model reproduced
infants’ discrimination of [r] et [je], but the units it learned did not resemble phonetic categories.
5 A complete model would also need to incorporate social factors (Conboy et al., 2015; Kuhl et al., 2003; Lytle
et coll., 2018; Tripp et al., 2021).
OPEN MIND: Discoveries in Cognitive Science
123
je
D
o
w
n
o
un
d
e
d
F
r
o
m
h
t
t
p
:
/
/
d
je
r
e
c
t
.
m
je
t
.
/
e
d
toi
o
p
m
je
/
je
un
r
t
je
c
e
–
p
d
F
/
d
o
je
/
je
/
.
/
1
0
1
1
6
2
o
p
m
_
un
_
0
0
0
4
6
1
9
6
9
1
5
7
o
p
m
_
un
_
0
0
0
4
6
p
d
.
/
je
F
b
oui
g
toi
e
s
t
t
o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3
Do Infants Learn Phonetic Categories?
Feldman et al.
Matusevych et al. (2020) found that a recurrent neural network that optimized its hidden repre-
sentations to represent correspondences between tokens of the same word achieved perfor-
mance comparable to the model from Schatz et al. (2021). The success of these models
suggests that alternatives to the phonetic category learning hypothesis, including perceptual
space learning models that have no sub-word categories at all, are well worth exploring. Dans
contraste, we are not aware of a phonetic category–based model that has been trained on con-
tinuous, unsegmented speech and used to predict cross-linguistic patterns of infants’ discrimi-
nation (see Schatz et al., 2021, supplementary discussion 1, for further discussion of this gap in
the literature).
Parallels between the phonetic learning and machine learning literatures provide other rea-
sons to be optimistic about perceptual space learning theories. Perceptual space learning
algorithms that rely on word-like units (Kamper et al., 2015; Renshaw et al., 2015; Riad
et coll., 2018; Thiollière et al., 2015) are reminiscent of proposals that the words infants segment
from fluent speech can constrain phonetic category learning (Feldman, Griffiths, et coll., 2013;
Swingley, 2009). The distributional learning strategy that Schatz et al. (2021) used is similar to
that proposed by Maye et al. (2002) to learn phonetic categories. Both of these strategies have
struggled to scale to more realistic data under a phonetic category learning account
(Antetomaso et al., 2017; Bion et al., 2013; Taniguchi et al., 2016), but perform well once
the constraint that phonetic categories need to be learned is dropped.
Jusczyk (1992) proposed over 25 years ago that phonetic learning might not rely on pho-
netic categories, but this idea has largely been disregarded in the literature on phonetic learn-
ing. Here we have argued that this idea is consistent with a large body of empirical literature
on infant phonetic learning and have connected the proposal to recent trends in speech tech-
nology that provide paths toward a formal theory. The time course of phonetic category learn-
ing has major implications for our understanding of language acquisition as a whole, and as
such we hope this article will inspire serious consideration of the perceptual space learning
hypothesis and encourage the kind of rigorous empirical and computational tests that can
ultimately distinguish it from the currently popular alternative.
REMERCIEMENTS
je
D
o
w
n
o
un
d
e
d
F
r
o
m
h
t
t
p
:
/
/
d
je
r
e
c
t
.
m
je
t
.
/
e
d
toi
o
p
m
je
/
je
un
r
t
je
c
e
–
p
d
F
/
d
o
je
/
je
/
/
.
1
0
1
1
6
2
o
p
m
_
un
_
0
0
0
4
6
1
9
6
9
1
5
7
o
p
m
_
un
_
0
0
0
4
6
p
d
.
/
je
F
b
oui
g
toi
e
s
t
t
o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3
We thank Adam Albright, Richard Aslin, Yevgen Matusevych, Bob McMurray, and two anon-
ymous reviewers for insightful comments.
INFORMATIONS SUR LE FINANCEMENT
NHF, National Science Foundation (https://dx.doi.org/10.13039/100000001), Award ID: BCS-
1734245. SG, Economic and Social Research Council (https://dx.doi.org/10.13039
/501100000269), Award ID: ES/R006660/1. SG, James S. McDonnell Foundation (https://dx
.doi.org/10.13039/100000913), Award ID: Scholar Award 220020374. ED, Agence
Nationale pour la Recherche, Award ID: ANR-17-EURE-0017 Frontcog. ED, Agence
Nationale pour la Recherche, Award ID: ANR-10-IDEX-0001-02 PSL*. ED, Agence Nationale
pour la Recherche, Award ID: ANR-19-P3IA-0001 PRAIRIE 31A Institute. ED, Facebook AI
Research, Award ID: Research Grant.
CONTRIBUTIONS DES AUTEURS
NHF: Conceptualisation: Lead; Acquisition de financement: Lead; Enquête: Lead; Writing – orig-
inal draft: Lead; Rédaction – révision & édition: Lead. SG: Conceptualisation: Lead; Funding acqui-
sition: Lead; Enquête: Lead; Rédaction – ébauche originale: Lead; Rédaction – révision & édition: Lead.
OPEN MIND: Discoveries in Cognitive Science
124
Do Infants Learn Phonetic Categories?
Feldman et al.
ED: Conceptualisation: Supporting; Acquisition de financement: Supporting; Rédaction – révision & edit-
ing: Supporting. TS: Conceptualisation: Supporting; Rédaction – révision & édition: Supporting.
RÉFÉRENCES
Adriaans, F., & Swingley, D. (2012). Distributional learning of
vowel categories is supported by prosody in infant-directed
speech. In N. Miyake, D. Peebles, & R.. P.. Tonnelier (Éd.),
Proceedings of the 34th Annual Conference of the Cognitive
Science Society (pp. 72–77). Cognitive Science Society.
Adriaans, F., & Swingley, D. (2017). Prosodic exaggeration within
infant-directed speech: Consequences for vowel learnability.
Journal of the Acoustical Society of America, 141(3070),
3070–3078. https://doi.org/10.1121/1.4982246, PubMed:
28599541
Anderson, J.. L., Morgan, J.. L., & Blanc, K. S. (2003). A statistical basis
for speech sound discrimination. Language and Speech, 46(2–3),
155–182. https://doi.org/10.1177/00238309030460020601,
PubMed: 14748443
Antetomaso, S., Miyazawa, K., Feldman, N., Elsner, M., Hitczenko,
K., & Mazuka, R.. (2017). Modeling phonetic category learning
from natural acoustic data. En M. LaMendola & J.. Scott (Éd.),
Proceedings of the 41st Boston University Conference on
Language Development (pp. 32–35). Cascadilla Press.
Apfelbaum, K. S., & McMurray, B. (2011). Using variability to guide
dimensional weighting: Associative mechanisms in early word
learning. Sciences cognitives, 35(6), 1105–1138. https://doi.org
/10.1111/j.1551-6709.2011.01181.x, PubMed: 21609356
Baevski, UN., Auli, M., & Mohamed, UN. (2019). Effectiveness of self-
supervised pre-training for speech recognition. ArXiv. https://
arxiv.org/abs/1911.03912
Baevski, UN., Schneider, S., & Auli, M.. (2019). vq-wav2vec: Self-
supervised learning of discrete speech representations. Dans
International Conference on Learning Representations.
OpenReview.net.
Baevski, UN., Zhou, Y., Mohamed, UN., & Auli, M.. (2020). wav2vec
2.0: A framework for self-supervised learning of speech represen-
tations. In H. Larochelle, M.. Ranzato, R.. Hadsell, M.. F. Balcan, &
H. Lin (Éd.), Advances in Neural Information Processing Systems
33 (pp. 12449–12460). Curran Associates.
Barascud, N., Pearce, M.. T., Griffiths, T. D., Friston, K. J., & Chait,
M.. (2016). Brain responses in humans reveal ideal observer-like
sensitivity to complex acoustic patterns. Actes du
Académie nationale des sciences, 113(5), E616–E625. https://est ce que je
.org/10.1073/pnas.1508523113, PubMed: 26787854
Bard, E. G., & Anderson, UN. H. (1982). The unintelligibility of
speech to children. Journal of Child Language, 10(2), 265–292.
https://doi.org/10.1017/S0305000900007777, PubMed:
6874768
Bergelson, E., & Swingley, D. (2012). At 6–9 months, human infants
know the meanings of many common nouns. Actes du
Académie nationale des sciences, 109(9), 3253–3258. https://est ce que je
.org/10.1073/pnas.1113380109, PubMed: 22331874
Best, C. T. (1994). Emergence of native-language influences. In J. C.
Homme bon & H. C. Nusbaum (Éd.), The development of speech
perception: The transition from speech sounds to spoken words
(pp. 167–224). AVEC Presse.
Best, C. T., & McRoberts, G. W. (2003). Infant perception of non-
native consonant contrasts that adults assimilate in different
ways. Language and Speech, 46(2–3), 183–216. https://doi.org
/10.1177/00238309030460020701, PubMed: 14748444
Best, C. T., McRoberts, G. W., LaFleur, R., & Silver-Isenstadt, J..
(1995). Divergent developmental patterns for infants’ perception
of two nonnative consonant contrasts. Infant Behavior and
Développement, 18(3), 339–350. https://doi.org/10.1016/0163
-6383(95)90022-5
Best, C. T., McRoberts, G. W., & Sithole, N. M.. (1988). Examination
of perceptual reorganization for nonnative speech contrasts: Zulu
click discrimination by English-speaking adults and infants.
Journal de psychologie expérimentale: Human Perception and
Performance, 14(3), 345–360. https://doi.org/10.1037/0096
-1523.14.3.345, PubMed: 2971765
Bion, R.. UN. H., Miyazawa, K., Kikuchi, H., & Mazuka, R.. (2013).
Learning phonemic vowel length from naturalistic recordings of
Japanese infant-directed speech. PLoS ONE, 8(2), Article e51594.
https://doi.org/10.1371/journal.pone.0051594, PubMed:
23437036
Bonnasse-Gahot, L., & Nadal, J.-P. (2008). Neural coding of cat-
egories: information efficiency and optimal population codes.
Journal of Computational Neuroscience, 25(1), 169–187.
https://doi.org/10.1007/s10827-007-0071-5, PubMed:
18236147
Bortfeld, H., Morgan, J.. L., Golinkoff, R.. M., & Rathbun, K. (2005).
Mommy and me: Familiar names help launch babies into
speech-stream segmentation. Sciences psychologiques, 16(4),
298–304. https://doi.org/10.1111/j.0956-7976.2005.01531.X,
PubMed: 15828977
Bosch, L., & Sebastián-Gallés, N. (2003). Simultaneous bilingualism
and the perception of a language-specific vowel contrast in the first
year of life. Language and Speech, 46(2–3), 217–243. https://est ce que je
.org/10.1177/00238309030460020801, PubMed: 14748445
Burnham, D. K. (1986). Developmental loss of speech perception:
Exposure to and experience with a first language. Applied
Psycholinguistics, 7(3), 207–240. https://est ce que je.org/10.1017
/S0142716400007542
Burnham, D. K., Earnshaw, L. J., & Clark, J.. E. (1991). Développement
of categorical identification of native and non-native bilabial
stops: Infants, children and adults. Journal of Child Language,
18, 231–260. https://doi.org/10.1017/S0305000900011041,
PubMed: 1874826
Brûlures, T. C., Yoshida, K. UN., Hill, K., & Werker, J.. F. (2007). Le
development of phonetic representation in bilingual and mono-
lingual infants. Applied Psycholinguistics, 28(3), 455–474.
https://doi.org/10.1017/S0142716407070257
Chen, F., Peng, G., Yan, N., & Wang, L. (2017). The development of
categorical perception of Mandarin tones in four- to seven-year-old
enfants. Journal of Child Language, 44(6), 1413–1434. https://
doi.org/10.1017/S0305000916000581, PubMed: 27916015
Chládková, K., & Paillereau, N. (2020). The what and when of uni-
versal perception: A review of early speech sound acquisition.
Language Learning, 70(4), 1136–1182. https://doi.org/10.1111
/lang.12422
Chorowski, J., Blanc, R.. J., Bengio, S., & van den Oord, UN. (2019).
Unsupervised speech representation learning using wavenet
autoencoders. IEEE/ACM Transactions on Audio, Speech, et
Language Processing, 27(12), 2041–2053. https://est ce que je.org/10
.1109/TASLP.2019.2938863
OPEN MIND: Discoveries in Cognitive Science
125
je
D
o
w
n
o
un
d
e
d
F
r
o
m
h
t
t
p
:
/
/
d
je
r
e
c
t
.
m
je
t
.
/
e
d
toi
o
p
m
je
/
je
un
r
t
je
c
e
–
p
d
F
/
d
o
je
/
je
/
/
.
1
0
1
1
6
2
o
p
m
_
un
_
0
0
0
4
6
1
9
6
9
1
5
7
o
p
m
_
un
_
0
0
0
4
6
p
d
/
.
je
F
b
oui
g
toi
e
s
t
t
o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3
Do Infants Learn Phonetic Categories?
Feldman et al.
Chung, Y.-A., Hsu, W.-N., Tang, H., & Verre, J.. (2019). An unsuper-
vised autoregressive model for speech representation learning. Dans
Proceedings of Interspeech (pp. 146–150). International Speech
Communication Association. https://doi.org/10.21437
/Interspeech.2019-1473
Conboy, B. T., Brooks, R., Meltzoff, UN. N., & Kuhl, P.. K. (2015).
Social interaction in infants’ learning of second-language pho-
netics: An exploration of brain-behavior relations. Developmental
Neuropsychology, 40(4), 216–229. https://est ce que je.org/10.1080
/87565641.2015.1014487, PubMed: 26179488
Cristia, UN. (2011). Fine-grained variation in caregivers’ /s/ predicts
their infants’ /s/ category. Journal of the Acoustical Society of
America, 129(5), 3271–3280. https://doi.org/10.1121/1
.3562562, PubMed: 21568428
Cristia, UN. (2018). Can infants learn phonology in the lab? A meta-
analytic answer. Cognition, 170, 312–327. https://est ce que je.org/10
.1016/j.cognition.2017.09.016, PubMed: 29102857
Cristia, UN., & Seidl, UN. (2014). The hyperarticulation hypothesis
of infant-directed speech. Journal of Child Language, 41(4),
913–934. https://doi.org/10.1017/S0305000912000669,
PubMed: 23406830
Curtin, S., Fennell, C., & Escudero, P.. (2009). Weighting of vowel
cues explains patterns of word-object associative learning.
Developmental Science, 12(5), 725–731. https://est ce que je.org/10
.1111/j.1467-7687.2009.00814.x, PubMed: 19702765
de Boer, B., & Kuhl, P.. K. (2003). Investigating the role of infant-
directed speech with a computer model. Acoustics Research
Letters Online, 4(4), 129–134. https://doi.org/10.1121/1.1613311
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT:
Pre-training of deep bidirectional transformers for language un-
derstanding. In Proceedings of the 2019 Conference of the
North American Chapter of the Association for Computational
Linguistics: Human Language Technologies, Volume 1 (Long and
Short Papers) (pp. 4171–4186). Association for Computational
Linguistics.
Dillon, B., Dunbar, E., & Idsardi, W. (2013). A single stage
approach to learning phonological categories: Insights from
Inuktitut. Sciences cognitives, 37(4), 344–377. https://est ce que je.org/10
.1111/cogs.12008, PubMed: 23137418
Dunbar, E., Algayres, R., Karadayi, J., Bernard, M., Benjumea, J.,
Cao, X.-N., Miskic, L., Dugrain, C., Ondel, L., Noir, UN. W.,
Besacier, L., Sakti, S., & Dupoux, E. (2019). The zero resource
speech challenge 2019: TTS without T. In Interspeech 2019: 20ème
Annual Congress of the International Speech Communication
Association. https://doi.org/10.21437/Interspeech.2019-2904
Dunbar, E., Cao, X. N., Benjumea, J., Karadayi, J., Bernard, M.,
Besacier, L., Anguera, X., & Dupoux, E. (2017). The zero
resource speech challenge 2017. Dans 2017 IEEE Automatic Speech
Recognition and Understanding Workshop (pp. 323–330). IEEE.
https://doi.org/10.1109/ASRU.2017.8268953
Eaves, B. S., Jr., Feldman, N. H., Griffiths, T. L., & Shafto, P.. (2016).
Infant-directed speech is consistent with teaching. Psychological
Review, 123(6), 758–771. https://doi.org/10.1037/rev0000031,
PubMed: 27088361
Eilers, R.. E., Gavin, W., & Wilson, W. R.. (1979). Linguistic experi-
ence and phonemic perception in infancy: A crosslinguistic
étude. Child Development, 50(1), 14–18. https://est ce que je.org/10
.2307/1129035, PubMed: 446199
Eimas, P.. D., Siqueland, E. R., Jusczyk, P., & Vigorito, J.. (1971).
Speech perception in infants. Science, 171(3968), 303–306.
https://doi.org/10.1126/science.171.3968.303, PubMed: 5538846
Erhan, D., Bengio, Y., Courville, UN., Manzagol, P.-A., Vincent, P.,
& Bengio, S. (2010). Why does unsupervised pre-training help
deep learning? Journal of Machine Learning Research, 11(19),
625–660.
Feldman, N. H., Griffiths, T. L., Goldwater, S., & Morgan, J.. L.
(2013). A role for the developing lexicon in phonetic category
acquisition. Psychological Review, 120(4), 751–778. https://est ce que je
.org/10.1037/a0034245, PubMed: 24219848
Feldman, N. H., Griffiths, T. L., & Morgan, J.. L. (2009). The influ-
ence of categories on perception: Explaining the perceptual
magnet effect as optimal statistical inference. Psychological
Review, 116(4), 752–782. https://doi.org/10.1037/a0017196,
PubMed: 19839683
Feldman, N. H., Myers, E. B., Blanc, K. S., Griffiths, T. L., &
Morgan, J.. L. (2013). Word-level information influences phonetic
learning in adults and infants. Cognition, 127(3), 427–438.
https://doi.org/10.1016/j.cognition.2013.02.007, PubMed:
23562941
Fennell, C. T., & Werker, J.. F. (2003). Early word learners’ ability to
access phonetic detail in well-known words. Language and
S p e e c h , 4 6 ( 2 ) , 2 4 5 – 2 6 4 . h t t p s : / / d o i . o r g / 1 0 . 1 1 7 7
/00238309030460020901, PubMed: 14748446
Flege, J.. E., & Hillenbrand, J.. (1986). Differential use of temporal
cues to the /s/-/z/ contrast by native and non-native speakers of
English. Journal of the Acoustical Society of America, 79(2),
508–517. https://doi.org/10.1121/1.393538, PubMed: 3950204
Francis, UN. L., Baldwin, K., & Nusbaum, H. C. (2000). Effects of
training on attention to acoustic cues. Perception et
Psychophysics, 62(8), 1668–1680. https://doi.org/10.3758
/BF03212164, PubMed: 11140187
Francis, UN. L., Kaganovich, N., & Driscoll-Huber, C. (2008). Cue-
specific effects of categorization training on the relative weight-
ing of acoustic cues to consonant voicing in English. Journal de
the Acoustical Society of America, 124(2), 1234–1251. https://
doi.org/10.1121/1.2945161, PubMed: 18681610
Francis, UN. L., & Nusbaum, H. C. (2002). Selective attention and
the acquisition of new phonetic categories. Journal d'expérimentation
Psychologie: Perception humaine et performance, 28(2), 349–366.
https://doi.org/10.1037/0096-1523.28.2.349, PubMed: 11999859
Frank, S., Feldman, N. H., & Goldwater, S. (2014). Weak semantic
context helps phonetic learning in a model of infant language
acquisition. In Proceedings of the 52nd Annual Meeting of the
Association for Computational Linguistics (pp. 1073–1083).
Association for Computational Linguistics. https://est ce que je.org/10
.3115/v1/P14-1101
Galle, M.. E., & McMurray, B. (2014). The development of voicing
catégories: A quantitative review of over 40 years of infant
speech perception research. Psychonomic Bulletin and Review,
21(4), 884–906. https://doi.org/10.3758/s13423-013-0569-y,
PubMed: 24550074
Gauthier, B., Shi, R., & Xu, Oui. (2007). Learning phonetic categories
by tracking movements. Cognition, 103(1), 80–106. https://est ce que je
.org/10.1016/j.cognition.2006.03.002, PubMed: 16650399
Goldstone, R.. (1994). Influences of categorization on perceptual
discrimination. Journal de psychologie expérimentale: General,
123(2), 178–200. https://doi.org/10.1037/0096-3445.123.2.178,
PubMed: 8014612
Greenlee, M.. (1980). Learning the phonetic cues to the voiced-
voiceless distinction: A comparison of child and adult speech.
Journal of Child Language, 7(3), 459–468. https://est ce que je.org/10
.1017/S0305000900002786, PubMed: 7440672
Grieser, D., & Kuhl, P.. K. (1989). Categorization of speech by in-
fants: Support for speech-sound prototypes. Developmental
Psychologie, 25(4), 577–588. https://doi.org/10.1037/0012-1649
.25.4.577
OPEN MIND: Discoveries in Cognitive Science
126
je
D
o
w
n
o
un
d
e
d
F
r
o
m
h
t
t
p
:
/
/
d
je
r
e
c
t
.
m
je
t
.
/
e
d
toi
o
p
m
je
/
je
un
r
t
je
c
e
–
p
d
F
/
d
o
je
/
je
/
/
.
1
0
1
1
6
2
o
p
m
_
un
_
0
0
0
4
6
1
9
6
9
1
5
7
o
p
m
_
un
_
0
0
0
4
6
p
d
/
.
je
F
b
oui
g
toi
e
s
t
t
o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3
Do Infants Learn Phonetic Categories?
Feldman et al.
Guenther, F. H., & Gjaja, M.. N. (1996). The perceptual magnet ef-
fect as an emergent property of neural map formation. Journal de
the Acoustical Society of America, 100(2), 1111–1121. https://
doi.org/10.1121/1.416296, PubMed: 8759964
Hazan, V., & Barrett, S. (2000). The development of phonemic cat-
egorization in children aged 6–12. Journal of Phonetics, 28(4),
377–396. https://doi.org/10.1006/jpho.2000.0121
Herrmann, M., Bauer, H.-U., & Der, R.. (1995). The “perceptual
magnet” effect: A model based on self-organizing feature maps.
In L. S. Forgeron & P.. J.. B. Hancock (Éd.), Proceedings of the 3rd
Neural Computation and Psychology Workshop (pp. 107–116).
Springer. https://doi.org/10.1007/978-1-4471-3579-1_9
Hitczenko, K., Mazuka, R., Elsner, M., & Feldman, N. H. (2020).
When context is and isn’t helpful: A corpus study of naturalistic
speech. Psychonomic Bulletin and Review, 27(4), 640–676.
https://doi.org/10.3758/s13423-019-01687-6, PubMed:
32166605
Hochmann, J.-R., & Bouffe, L. (2014). The invariance problem in
infancy: A pupillometry study. Sciences psychologiques, 25(11),
2038–2046. https://doi.org/10.1177/0956797614547918,
PubMed: 25269621
Holt, L. L., & Loto, UN. J.. (2006). Cue weighting in auditory cate-
gorization: Implications for first and second language acquisi-
tion. Journal of the Acoustical Society of America, 119(5),
3059–3071. https://doi.org/10.1121/1.2188377, PubMed:
16708961
Holt, L. L., Loto, UN. J., & Kluender, K. R.. (2001). Influence of fun-
damental frequency on stop-consonant voicing perception: UN
case of learned covariation or auditory enhancement? Journal
of the Acoustical Society of America, 109(2), 764–774. https://
doi.org/10.1121/1.1339825, PubMed: 11248980
Hoonhorst, JE., Colin, C., Markessis, E., Radeau, M., Deltenre, P., &
Serniclaes, W. (2009). French native speakers in the making:
From language-general to language-specific voicing boundaries.
Journal of Experimental Child Psychology, 104(4), 353–366.
https://doi.org/10.1016/j.jecp.2009.07.005, PubMed: 19709671
Hsu, W.-N., Bolte, B., Tsai, Y.-H. H., Lakhotia, K., Salakhutdinov,
R., & Mohamed, UN. (2021). HuBERT: Self-supervised speech
representation learning by masked prediction of hidden units.
ArXiv. https://arxiv.org/abs/2106.07447
Idemaru, K., & Holt, L. L. (2011). Word recognition reflects dimension-
based statistical learning. Journal de psychologie expérimentale:
Perception humaine et performance, 37(6), 1939–1956. https://
doi.org/10.1037/a0025641, PubMed: 22004192
Idemaru, K., & Holt, L. L. (2014). Specificity of dimension-based
statistical learning in word recognition. Journal d'expérimentation
Psychologie: Perception humaine et performance, 40(3), 1009–1021.
https://doi.org/10.1037/a0035269, PubMed: 24364708
Iverson, P., Kuhl, P.. K., Akahane-Yamada, R., Diesch, E., Tohkura,
Y., Kettermann, UN., & Siebert, C. (2003). A perceptual interfer-
ence account of acquisition difficulties for non-native phonemes.
Cognition, 87(1), B47–B57. https://doi.org/10.1016/S0010-0277
(02)00198-1, PubMed: 12499111
Jansen, UN., & Van Durme, B. (2011). Efficient spoken term discovery
using randomized algorithms. In IEEE Workshop on Automatic
Speech Recognition and Understanding (pp. 401–406). IEEE.
https://doi.org/10.1109/ASRU.2011.6163965
Jones, C., Meakins, F., & Muawiyath, S. (2012). Learning vowel
categories from maternal speech in Gurindji Kriol. Language
Apprentissage, 62(4), 1052–1078. https://doi.org/10.1111/j.1467
-9922.2012.00725.X
Jusczyk, P.. W. (1992). Developing phonological categories from the
speech signal. In C. UN. Ferguson, L. Menn, & C. Stoel-Gammon
(Éd.), Phonological development: Models, recherche, implications
(pp. 17–64). York.
Jusczyk, P.. W., & Aslin, R.. N. (1995). Infants’ detection of the sound
patterns of words in fluent speech. Psychologie Cognitive, 29(1),
1–23. https://doi.org/10.1006/cogp.1995.1010, PubMed: 7641524
Jusczyk, P.. W., Houston, D. M., & Newsome, M.. (1999). The begin-
nings of word segmentation in English-learning infants. Cognitive
Psychologie, 39(3–4), 159–207. https://doi.org/10.1006/cogp
.1999.0716, PubMed: 10631011
Kamper, H., Elsner, M., Jansen, UN., & Goldwater, S. (2015).
Unsupervised neural network based feature extraction using
weak top-down constraints. In Proceedings of the 40th IEEE
International Conference on Acoustics, Speech and Signal
Processing (pp. 5818–5822). IEEE. https://doi.org/10.1109
/ICASSP.2015.7179087
Kingston, J., Diehl, R.. L., Kirk, C. J., & Castleman, W. UN. (2008). On the
internal perceptual structure of distinctive features: Le [voice]
contraste. Journal of Phonetics, 36(1), 28–54. https://est ce que je.org/10
.1016/j.wocn.2007.02.001, PubMed: 19657466
Kohonen, T. (1989). Self-organization and associative memory.
Springer. https://doi.org/10.1007/978-3-642-88163-3
Kohonen, T. (2001). Self-organizing maps (3rd ed.). Springer.
https://doi.org/10.1007/978-3-642-56927-2
Krause, S. E. (1982). Vowel duration as a perceptual cue to postvo-
calic consonant voicing in young children and adults. Journal de
the Acoustical Society of America, 71(4), 990–995. https://est ce que je
.org/10.1121/1.387580, PubMed: 7085987
Kronrod, Y., Coppess, E., & Feldman, N. H. (2016). A unified ac-
count of categorical effects in phonetic perception. Psychonomic
Bulletin and Review, 23(6), 1681–1712. https://doi.org/10.3758
/s13423-016-1049-y, PubMed: 27220996
Kruschke, J.. K. (1992). ALCOVE: An exemplar-based connectionist
model of category learning. Psychological Review, 99(1), 22–44.
https://doi.org/10.1037/0033-295X.99.1.22, PubMed: 1546117
Kuhl, P.. K. (1979). Speech perception in early infancy: Perceptual
constancy for spectrally dissimilar vowel categories. Journal de
the Acoustical Society of America, 66(6), 1668–1679. https://
doi.org/10.1121/1.383639, PubMed: 521551
Kuhl, P.. K. (1981). Discrimination of speech by nonhuman animals:
Basic auditory sensitivities conducive to the perception of
speech-sound categories. Journal of the Acoustical Society of
America, 70(2), 340–349. https://doi.org/10.1121/1.386782
Kuhl, P.. K. (1983). Perception of auditory equivalence classes for
speech in early infancy. Infant Behavior and Development, 6(2–3),
263–285. https://doi.org/10.1016/S0163-6383(83)80036-8
Kuhl, P.. K. (1991). Human adults and human infants show a “per-
ceptual magnet effect” for the prototypes of speech categories,
monkeys do not. Perception and Psychophysics, 50(2), 93–107.
https://doi.org/10.3758/BF03212211, PubMed: 1945741
Kuhl, P.. K. (1993). Early linguistic experience and phonetic percep-
tion: Implications for theories of developmental speech perception.
Journal of Phonetics, 21(1–2), 125–139. https://est ce que je.org/10.1016
/S0095-4470(19)31326-9
Kuhl, P.. K., Andruski, J.. E., Chistovich, je. UN., Chistovich, L. UN.,
Kozhevnikova, E. V., Ryskina, V. L., Stolyarova, E. JE., Sundberg,
U., & Lacerda, F. (1997). Cross-language analysis of phonetic
units in language addressed to infants. Science, 277(5326),
684–686. https://doi.org/10.1126/science.277.5326.684,
PubMed: 9235890
Kuhl, P.. K., & Miller, J.. D. (1975). Speech perception by the chin-
chilla: Voiced-voiceless distinction in alveolar plosive conso-
nants. Science, 190(4209), 69–72. https://doi.org/10.1126
/science.1166301, PubMed: 1166301
OPEN MIND: Discoveries in Cognitive Science
127
je
D
o
w
n
o
un
d
e
d
F
r
o
m
h
t
t
p
:
/
/
d
je
r
e
c
t
.
m
je
t
.
/
e
d
toi
o
p
m
je
/
je
un
r
t
je
c
e
–
p
d
F
/
d
o
je
/
je
/
/
.
1
0
1
1
6
2
o
p
m
_
un
_
0
0
0
4
6
1
9
6
9
1
5
7
o
p
m
_
un
_
0
0
0
4
6
p
d
/
.
je
F
b
oui
g
toi
e
s
t
t
o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3
Do Infants Learn Phonetic Categories?
Feldman et al.
Kuhl, P.. K., & Padden, D. M.. (1982). Enhanced discriminability at
the phonetic boundaries for the voicing feature in macaques.
Perception and Psychophysics, 32(6), 542–550. https://doi.org
/10.3758/BF03204208, PubMed: 7167352
Kuhl, P.. K., Stevens, E., Hayashi, UN., Deguchi, T., Kiritani, S., &
Iverson, P.. (2006). Infants show a facilitation effect for native
language phonetic perception between 6 et 12 mois.
Developmental Science, 9(2), F13–F21. https://doi.org/10.1111
/j.1467-7687.2006.00468.x, PubMed: 16472309
Kuhl, P.. K., Tsao, F.-M., & Liu, H.-M. (2003). Foreign-language
experience in infancy: Effects of short-term exposure and social
interaction on phonetic learning. Actes de la Nationale
Académie des Sciences, 100(15), 9096–9101. https://est ce que je.org/10
.1073/pnas.1532872100, PubMed: 12861072
Kuhl, P.. K., Williams, K. UN., Lacerda, F., Stevens, K. N., & Lindblom,
B. (1992). Linguistic experience alters phonetic perception in in-
fants by 6 months of age. Science, 255(5044), 606–608. https://
doi.org/10.1126/science.1736364, PubMed: 1736364
Kuijpers, C. T. L. (1996). Perception of the voicing contrast by
Dutch children and adults. Journal of Phonetics, 24(3), 367–382.
https://doi.org/10.1006/jpho.1996.0020
Lacerda, F. (1995). The perceptual-magnet effect: An emergent
consequence of exemplar-based phonetic memory. In K. Elenius &
P.. Branderud (Éd.), Proceedings of the XIIIth International Congress
of Phonetic Sciences (Vol. 2, pp. 140–147). KTH and Stockholm
University.
Lee, C.-Y., O’Donnell, T. J., & Verre, J.. R.. (2015). Unsupervised
lexicon discovery from acoustic input. Transactions of the
Association for Computational Linguistics, 3, 389–403. https://
doi.org/10.1162/tacl_a_00146
Lee, S., & Katz, J.. (2016). Perceptual integration of acoustic cues to
laryngeal contrasts in Korean fricatives. Journal of the Acoustical
Society of America, 139(2), 605–611. https://doi.org/10.1121/1
.4926435, PubMed: 26936544
Lehet, M., & Holt, L. L. (2017). Dimension-based statistical learning
affects both speech perception and production. Cognitive
Science, 41(S1), 885–912. https://doi.org/10.1111/cogs.12413,
PubMed: 27666146
Lehet, M., & Holt, L. L. (2020). Nevertheless, it persists: Dimension-
based statistical learning and normalization of speech impact dif-
ferent levels of perceptual processing. Cognition, 202, Article
104328. https://doi.org/10.1016/j.cognition.2020.104328,
PubMed: 32502867
Liberman, UN. M., Harris, K. S., Hoffman, H. S., & Griffith, B. C.
(1957). The discrimination of speech sounds within and across
phoneme boundaries. Journal de psychologie expérimentale, 54(5),
358–368. https://doi.org/10.1037/h0044417, PubMed: 13481283
Lim, S.-J., Fiez, J.. UN., & Holt, L. L. (2019). Role of the striatum in
incidental learning of sound categories. Actes du
Académie nationale des sciences, 116(10), 4671–4680. https://est ce que je
.org/10.1073/pnas.1811992116, PubMed: 30782817
Lim, S.-J., & Holt, L. L. (2011). Learning foreign sounds in an alien
monde: Videogame training improves non-native speech catego-
rization. Sciences cognitives, 35(7), 1390–1405. https://est ce que je.org/10
.1111/j.1551-6709.2011.01192.x, PubMed: 21827533
Lipski, S. C., Escudero, P., & Benders, T. (2012). Language experi-
ence modulates weighting of acoustic cues for vowel perception:
An event-related potential study. Psychophysiology, 49(5),
638–650. https://doi.org/10.1111/j.1469-8986.2011.01347.x,
PubMed: 22335401
Lisker, L., & Abramson, UN. S. (1964). A cross-language study of
voicing in initial stops: Acoustical measurements. Word, 20(3),
384–422. https://doi.org/10.1080/00437956.1964.11659830
Liu, L., & Kager, R.. (2014). Perception of tones by infants learning a
non-tone language. Cognition, 133(2), 385–394. https://doi.org
/10.1016/j.cognition.2014.06.004, PubMed: 25128796
Liu, L., & Kager, R.. (2016). Perception of a native vowel contrast by
Dutch monolingual and bilingual infants: A bilingual perceptual
lead. International Journal of Bilingualism, 20(3), 335–345.
https://doi.org/10.1177/1367006914566082
Liu, R., & Holt, L. L. (2015). Dimension-based statistical learning of
vowels. Journal de psychologie expérimentale: Human Perception
et performances, 41(6), 1783–1798. https://doi.org/10.1037
/xhp0000092, PubMed: 26280268
Love, B. C., Medin, D. L., & Gureckis, T. M.. (2004). SUSTAIN: UN
network model of category learning. Psychological Review,
111(2), 309–332. https://doi.org/10.1037/0033-295X.111.2.309,
PubMed: 15065912
Lytle, S. R., Garcia-Sierra, UN., & Kuhl, P.. K. (2018). Two are better
than one: Infant language learning from video improves in the
presence of peers. Proceedings of the National Academy of
les sciences, 115(40), 9859–9866. https://doi.org/10.1073/pnas
.1611621115, PubMed: 30275298
Mattock, K., & Burnham, D. (2006). Chinese and English infants’
tone perception: Evidence for perceptual reorganization.
I n f a n c y , 1 0 ( 3 ) , 2 4 1 – 2 6 5 . h t t p s : / / d o i . o r g / 1 0 . 1 2 0 7
/s15327078in1003_3
Matusevych, Y., Schatz, T., Kamper, H., Feldman, N. H., &
Goldwater, S. (2020). Evaluating computational models of infant
phonetic learning across languages. In S. Denison, M.. Mack, Oui.
Xu, & B. C. Armstrong (Éd.), Proceedings of the 42nd Annual
Conference of the Cognitive Science Society (pp. 571–577).
Cognitive Science Society.
Maye, J., Blanc, D. J., & Aslin, R.. N. (2008). Statistical phonetic
learning in infants: Facilitation and feature generalization.
Developmental Science, 11(1), 122–134. https://est ce que je.org/10
.1111/j.1467-7687.2007.00653.x, PubMed: 18171374
Maye, J., Werker, J.. F., & Gerken, L. (2002). Infant sensitivity to
distributional information can affect phonetic discrimination.
Cognition, 82(3), B101–B111. https://doi.org/10.1016/S0010
-0277(01)00157-3, PubMed: 11747867
Mazuka, R., Hasegawa, M., & Tsuji, S. (2014). Development of
non-native vowel discrimination: Improvement without expo-
sure. Developmental Psychobiology, 56(2), 192–209. https://est ce que je
.org/10.1002/dev.21193, PubMed: 24374789
McInnes, F. R., & Goldwater, S. (2011). Unsupervised extraction of
recurring words from infant-directed speech. In L. Carlson, C.
Hölscher, & T. Shipley (Éd.), Proceedings of the 33rd Annual
Conference of the Cognitive Science Society (pp. 2006–2011).
Cognitive Science Society.
McMurray, B., Aslin, R.. N., & Toscano, J.. C. (2009). Statistical
learning of phonetic categories: Insights from a computational
approche. Developmental Science, 12(3), 369–378. https://
doi.org/10.1111/j.1467-7687.2009.00822.x, PubMed:
19371359
McMurray, B., Danelz, UN., Rigler, H., & Seedorff, M.. (2018).
Speech categorization develops slowly through adolescence.
Developmental Psychobiology, 54(8), 1472–1491. https://doi.org
.org/10.1037/dev0000542, PubMed: 29952600
McMurray, B., Kovack-Lesh, K. UN., Goodwin, D., & McEchron, W.
(2013). Infant directed speech and the development of speech
perception: Enhancing development or an unintended conse-
quence? Cognition, 129(2), 362–378. https://est ce que je.org/10.1016/j
.cognition.2013.07.015, PubMed: 23973465
Medina, V., Hoonhorst, JE., Bogliotti, C., & Serniclaes, W. (2010).
Development of voicing perception in French: Comparing
OPEN MIND: Discoveries in Cognitive Science
128
je
D
o
w
n
o
un
d
e
d
F
r
o
m
h
t
t
p
:
/
/
d
je
r
e
c
t
.
m
je
t
.
/
e
d
toi
o
p
m
je
/
je
un
r
t
je
c
e
–
p
d
F
/
d
o
je
/
je
/
/
.
1
0
1
1
6
2
o
p
m
_
un
_
0
0
0
4
6
1
9
6
9
1
5
7
o
p
m
_
un
_
0
0
0
4
6
p
d
/
.
je
F
b
oui
g
toi
e
s
t
t
o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3
Do Infants Learn Phonetic Categories?
Feldman et al.
adultes, adolescents, and children. Journal of Phonetics, 38(4),
493–503. https://doi.org/10.1016/j.wocn.2010.06.002
Miyazawa, K., Kikuchi, H., & Mazuka, R.. (2010). Unsupervised
learning of vowels from continuous speech based on self-
In Proceedings of
organized phoneme acquisition model.
Interspeech (pp. 2914–2917). https://doi.org/10.21437/Interspeech
.2010-757
Miyazawa, K., Miura, H., Kikuchi, H., & Mazuka, R.. (2011). Le
multi timescale phoneme acquisition model of the self-
organizing based on the dynamic features. In Proceedings of
Interspeech (pp. 749–752). International Speech Communication
Association. https://doi.org/10.21437/Interspeech.2011-286
Moon, C., Lagercrantz, H., & Kuhl, P.. K. (2013). Language experi-
ence in utero affects vowel perception after birth: A two-country
étude. Acta Pediatrica, 102(2), 156–160. https://doi.org/10.1111
/apa.12098, PubMed: 23173548
Mugitani, R., Pons, F., Fais, L., Dietrich, C., Werker, J.. F., & Amano,
S. (2009). Perception of vowel length by Japanese- and English-
learning infants. Developmental Psychology, 45(1), 236–247.
https://doi.org/10.1037/a0014043, PubMed: 19210005
Narayan, C. R., Werker, J.. F., & Beddor, P.. S. (2010). The inter-
action between acoustic salience and language experience in
developmental speech perception: Evidence from nasal place
discrimination. Developmental Science, 13(3), 407–420.
https://doi.org/10.1111/j.1467-7687.2009.00898.x, PubMed:
20443962
Nearey, T. M.. (1997). Speech perception as pattern recognition.
Journal of the Acoustical Society of America, 101(6), 3241–3254.
https://doi.org/10.1121/1.418290, PubMed: 9193041
Nittrouer, S. (1992). Age-related differences in perceptual effects of
formant transitions within syllables and across syllable bound-
aries. Journal of Phonetics, 20(3), 351–382. https://est ce que je.org/10
.1016/S0095-4470(19)30639-4
Nittrouer, S., & Miller, M.. E. (1997). Predicting developmental shifts
in perceptual weighting schemes. Journal of the Acoustical
Society of America, 101(4), 2253–2266. https://doi.org/10.1121
/1.418207, PubMed: 9104027
Nittrouer, S., & Studdert-Kennedy, M.. (1987). The role of coarticula-
tory effects in the perception of fricatives by children and adults.
Journal of Speech and Hearing Research, 30(3), 319–329. https://
doi.org/10.1044/jshr.3003.319, PubMed: 3669639
Nixon, J.. S., & Tomaschek, F. (2021). Prediction and error in early
infant speech learning: A speech acquisition model. Cognition,
212, Article 104697. https://doi.org/10.1016/j.cognition.2021
.104697, PubMed: 33798952
Nosofsky, R.. M.. (1986). Attention, similarité, and the identification-
categorization relationship. Journal de psychologie expérimentale,
115(1), 39–57. https://doi.org/10.1037/0096-3445.115.1.39,
PubMed: 2937873
Ohde, R.. N., & Haley, K. L. (1997). Stop-consonant and vowel per-
ception in 3- and 4-year-old children. Journal of the Acoustical
Society of America, 102(6), 3711–3722. https://doi.org/10.1121
/1.420135, PubMed: 9407663
Ohde, R.. N., Haley, K. L., Vorperian, H. K., & McMahon, C. W.
(1995). A developmental study of the perception of onset spectra
for stop consonants in different vowel environments. Journal de
the Acoustical Society of America, 97(6), 3800–3812. https://
doi.org/10.1121/1.412395, PubMed: 7790658
Pajak, B., Bicknell, K., & Levy, R.. (2013). A model of generalization
in distributional learning of phonetic categories. In Proceedings
of the Fourth Annual Workshop on Cognitive Modeling and
Computational Linguistics (pp. 11–20). Association for Computational
Linguistics.
Parc, UN. S., & Verre, J.. R.. (2008). Unsupervised pattern discovery in
speech. IEEE Transactions on Audio, Speech and Language
Processing, 16(1), 186–197. https://doi.org/10.1109/TASL.2007
.909282
Pegg, J.. E., & Werker, J.. F. (1997). Adult and infant perception of
two English phones. Journal of the Acoustical Society of America,
102(6), 3742–3753. https://doi.org/10.1121/1.420137, PubMed:
9407666
Peters, M.. E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee,
K., & Zettlemoyer, L. (2018). Deep contextualized word represen-
tations. In Proceedings of the Conference of the North American
Chapter of the Association for Computational Linguistics: Human
Language Technologies (pp. 2227–2237). Association for
Computational Linguistics. https://doi.org/10.18653/v1/N18-1202
Polka, L., & Bohn, O.-S. (1996). A cross-language comparison of
vowel perception in English-learning and German-learning
infants. Journal of the Acoustical Society of America, 100(1),
577–592. https://doi.org/10.1121/1.415884, PubMed: 8675849
Polka, L., Colantonie, C., & Sundara, M.. (2001). A cross-language
comparison of /d/-/ð/ perception: Evidence for a new develop-
mental pattern. Journal of the Acoustical Society of America,
109(5), 2190–2201. https://doi.org/10.1121/1.1362689,
PubMed: 11386570
Pollack, JE., & Pickett, J.. M.. (1963). The intelligibility of excerpts
from conversation. Language and Speech, 6(3), 165–171.
https://doi.org/10.1177/002383096300600305
Ranzato, M., Poultney, C., Chopra, S., & Cun, Oui. (2007). Efficient
learning of sparse representations with an energy-based model.
In B. Schölkopf, J.. Platt, & T. Hoffman (Éd.), Advances in
Neural Information Processing Systems 19 (pp. 1137–1144).
AVEC Presse.
Räsänen, Ô. (2011). A computational model of word segmentation
from continuous speech using transitional probabilities of atomic
acoustic events. Cognition, 120(2), 149–176. https://est ce que je.org/10
.1016/j.cognition.2011.04.001, PubMed: 21524739
Räsänen, O., & Blandon, M.. C. (2020). Unsupervised discovery of
recurring speech patterns using probabilistic adaptive metrics. Dans
Proceedings of Interspeech (pp. 4871–4875). International
Speech Communication Association. https://doi.org/10.21437
/Interspeech.2020-1738
Räsänen, O., & Rasilo, H. (2015). A joint model of word segmen-
tation and meaning acquisition through cross-situational learn-
ing. Psychological Review, 122(4), 792–829. https://est ce que je.org/10
.1037/a0039702, PubMed: 26437151
Renshaw, D., Kamper, H., Jansen, UN., & Goldwater, S. (2015). UN
comparison of neural network methods for unsupervised repre-
sentation learning on the zero resource speech challenge. Dans
Proceedings of Interspeech. International Speech Communication
Association. https://doi.org/10.21437/Interspeech.2015-644
Riad, R., Dancette, C., Karadayi, J., Zeghidour, N., Schatz, T., &
Dupoux, E. (2018). Sampling strategies in Siamese Networks
for unsupervised speech representation learning. ArXiv. https://
arxiv.org/abs/1804.11297
Roark, C. L., & Holt, L. L. (2019). Perceptual dimensions influence
auditory category learning. Attention, Perception, et
Psychophysics, 81(4), 912–926. https://doi.org/10.3758/s13414
-019-01688-6, PubMed: 30761504
Roark, C. L., Plaut, D. C., & Holt, L. L. (2020). A neural network
model of the effect of prior experience with regularities on sub-
sequent category learning. In S. Denison, M.. Mack, Oui. Xu, &
B. C. Armstrong (Éd.), Proceedings of the 42nd Annual
Conference of the Cognitive Science Society (pp. 1817–1823).
Cognitive Science Society.
OPEN MIND: Discoveries in Cognitive Science
129
je
D
o
w
n
o
un
d
e
d
F
r
o
m
h
t
t
p
:
/
/
d
je
r
e
c
t
.
m
je
t
.
/
e
d
toi
o
p
m
je
/
je
un
r
t
je
c
e
–
p
d
F
/
d
o
je
/
je
.
/
/
1
0
1
1
6
2
o
p
m
_
un
_
0
0
0
4
6
1
9
6
9
1
5
7
o
p
m
_
un
_
0
0
0
4
6
p
d
/
.
je
F
b
oui
g
toi
e
s
t
t
o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3
Do Infants Learn Phonetic Categories?
Feldman et al.
Rost, G. C., & McMurray, B. (2009). Speaker variability augments
phonological processing in early word learning. Developmental
Science, 12(2), 339–349. https://doi.org/10.1111/j.1467-7687
.2008.00786.X, PubMed: 19143806
Saffran, J.. R., Aslin, R.. N., & Newport, E. L. (1996). Statistical learn-
ing by 8-month-old infants. Science, 274(5294), 1926–1928.
https://doi.org/10.1126/science.274.5294.1926, PubMed:
8943209
Scharinger, M., Henry, M.. J., & Obleser, J.. (2013). Prior experience
with negative spectral correlations promotes information integra-
tion during auditory category learning. Memory and Cognition,
41(5), 752–768. https://doi.org/10.3758/s13421-013-0294-9,
PubMed: 23354998
Schatz, T. (2016). ABX-discriminability measures and applications
(Unpublished doctoral dissertation). Université Paris 6.
Schatz, T., Bach, F., & Dupoux, E. (2018). Evaluating automatic
speech recognition systems as quantitative models of cross-
lingual phonetic category perception. Journal of the Acoustical
Society of America, 143(5), EL372–EL378. https://est ce que je.org/10
.1121/1.5037615, PubMed: 29857692
Schatz, T., & Feldman, N. H. (2018). Neural network vs. HMM
speech recognition systems as models of human cross-linguistic
phonetic perception. In Proceedings of the Conference on Cognitive
Computational Neuroscience. Cognitive Science Society. https://
doi.org/10.32470/CCN.2018.1240-0
Schatz, T., Feldman, N. H., Goldwater, S., Cao, X.-N., & Dupoux,
E. (2021). Early phonetic learning without phonetic categories.
Actes de l'Académie nationale des sciences, 118(7), Article
e2001844118. https://doi.org/10.1073/pnas.2001844118,
PubMed: 33510040
Schatz, T., Peddinti, V., Bach, F., Jansen, UN., Hermansky, H., &
Dupoux, E. (2013). Evaluating speech features with the
minimal-pair ABX task: Analysis of the classical MFC/PLP pipe-
Interspeech (pp. 1781–1785).
line.
International Speech Communication Association. https://est ce que je
.org/10.21437/Interspeech.2013-441
In Proceedings of
Schertz, J., Carbonell, K., & Loto, UN. J.. (2020). Language specificity
in phonetic cue weighting: Monolingual and bilingual perception
of the stop voicing contrast in English and Spanish. Phonetica,
77(3), 186–208. https://doi.org/10.1159/000497278, PubMed:
31018217
Schneider, S., Baevski, UN., Collobert, R., & Auli, M.. (2019). wav2vec:
Unsupervised pre-training for speech recognition. ArXiv. https://
arxiv.org/abs/1904.05862v4
Segal, O., Hejli-Assi, S., & Kishon-Rabin, L. (2016). The effect of
listening experience on the discrimination of / ba/ and /pa/ in
Hebrew-learning and Arabic-learning infants. Infant Behavior
and Development, 42, 86–99. https://doi.org/10.1016/j.infbeh
.2015.10.002, PubMed: 26708235
Shepard, R.. N. (1987). Toward a universal law of generalization for
psychological science. Science, 237(4820), 1317–1323. https://
doi.org/10.1126/science.3629243, PubMed: 3629243
Shi, L., Griffiths, T. L., Feldman, N. H., & Sanborn, UN. N. (2010).
Exemplar models as a mechanism for performing Bayesian infer-
ence. Psychonomic Bulletin and Review, 17(4), 443–464. https://
doi.org/10.3758/PBR.17.4.443, PubMed: 20702863
Simon, C., & Fourcin, UN. J.. (1978). Cross-language study of speech-
pattern learning. Journal of the Acoustical Society of America,
63(3), 925–935. https://doi.org/10.1121/1.381772
Slawinski, E. B., & Fitzgerald, L. K. (1998). Perceptual development
of the categorization of the /r-w/ contrast in normal children.
Journal of Phonetics, 26(1), 27–43. https://doi.org/10.1006/jpho
.1997.0057
Stager, C. L., & Werker, J.. F. (1997). Infants listen for more phonetic
detail in speech perception than in word-learning tasks. Nature,
388(6640), 381–382. https://doi.org/10.1038/41102, PubMed:
9237755
Stilp, C. E., & Kluender, K. R.. (2012). Efficient coding and statisti-
cally optimal weighting of covariance among acoustic attributes
in novel sounds. PLoS ONE, 7(1), Article e30845. https://doi.org
/10.1371/journal.pone.0030845, PubMed: 22292057
Stilp, C. E., Rogers, T. T., & Kluender, K. R.. (2010). Rapid efficient
coding of correlated complex acoustic properties. Proceedings of
the National Academy of Sciences, 107, 21914–21919. https://
doi.org/10.1073/pnas.1009020107, PubMed: 21098293
Streeter, L. UN. (1976). Language perception of 2-month-old infants
shows effects of both innate mechanisms and experience. Nature,
259(5538), 39–41. https://doi.org/10.1038/259039a0, PubMed:
1256541
Sundara, M., Polka, L., & Genesee, F. (2006). Language-experience
facilitates discrimination of /d-ð/ in monolingual and bilingual
acquisition of English. Cognition, 100(2), 369–388. https://est ce que je
.org/10.1016/j.cognition.2005.04.007, PubMed: 16115614
Swingley, D. (2009). Contributions of infant word learning to
language development. Philosophical Transactions of the Royal
Society B, 364(1536), 3617–3632. https://doi.org/10.1098/rstb
.2009.0107, PubMed: 19933136
Swingley, D. (2019). Learning phonology from surface distribu-
tion, considering Dutch and English vowel duration. Language
Learning and Development, 15(3), 199–216. https://est ce que je.org/10
.1080/15475441.2018.1562927, PubMed: 31607832
T a ni g u c h i , T . , N a g a s a k a , S . , & N a k a s h i m a , R. .
( 20 1 6 ) .
Nonparametric Bayesian double articulation analyzer for direct
language acquisition from continuous speech signals. IEEE
Transactions on Cognitive and Developmental Systems, 8(3),
171–185. https://doi.org/10.1109/TCDS.2016.2550591
Thiollière, R., Dunbar, E., Synnaeve, G., Versteegh, M., &
Dupoux, E. (2015). A hybrid dynamic time warping-deep neu-
ral network architecture for unsupervised acoustic modeling. Dans
Proceedings of Interspeech (pp. 3169–3173). International Speech
Communication Association. https://doi.org/10.21437/Interspeech
.2015-640
Toscano, J.. C., & McMurray, B. (2010). Cue integration with cate-
gories: Weighting acoustic cues in speech using unsupervised
learning and distributional statistics. Sciences cognitives, 34(3),
434–464. https://doi.org/10.1111/j.1551-6709.2009.01077.x,
PubMed: 21339861
Tricomi, E., Delgado, M.. R., McCandliss, B. D., McClelland, J.. L., &
Fiez, J.. UN. (2006). Performance feedback drives caudate activa-
tion in a phonological learning task. Journal of Cognitive
Neurosciences, 18(6), 1029–1043. https://doi.org/10.1162/jocn
.2006.18.6.1029, PubMed: 16839308
Tripp, UN., Feldman, N. H., & Idsardi, W. J.. (2021). Social inference
may guide early lexical learning. Frontiers in Psychology, 12,
Article 645247. https://doi.org/10.3389/fpsyg.2021.645247,
PubMed: 34093326
Trubetzkoy, N. S. (1939). Grundzüge der Phonologie. Vandenhoeck
und Ruprecht.
Tsao, F.-M., Liu, H.-M., & Kuhl, P.. K. (2006). Perception of native
and non-native affricate-fricative contrasts: Cross-language tests
on adults and infants. Journal of the Acoustical Society of America,
120(4), 2285–2294. https://doi.org/10.1121/1.2338290, PubMed:
17069324
Tsuji, S., & Cristia, UN. (2014). Perceptual attunement in vowels: UN
meta-analysis. Developmental Psychobiology, 56(2), 179–191.
https://doi.org/10.1002/dev.21179, PubMed: 24273029
OPEN MIND: Discoveries in Cognitive Science
130
je
D
o
w
n
o
un
d
e
d
F
r
o
m
h
t
t
p
:
/
/
d
je
r
e
c
t
.
m
je
t
.
/
e
d
toi
o
p
m
je
/
je
un
r
t
je
c
e
–
p
d
F
/
d
o
je
/
je
.
/
/
1
0
1
1
6
2
o
p
m
_
un
_
0
0
0
4
6
1
9
6
9
1
5
7
o
p
m
_
un
_
0
0
0
4
6
p
d
.
/
je
F
b
oui
g
toi
e
s
t
t
o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3
Do Infants Learn Phonetic Categories?
Feldman et al.
Underbakke, M., Polka, L., Gottfried, T. L., & Strange, W. (1988).
Trading relations in the perception of /r/-/l/ by Japanese learners
of English. Journal of the Acoustical Society of America, 84(1),
90–100. https://doi.org/10.1121/1.396878, PubMed: 3411058
Vallabha, G. K., McClelland, J.. L., Pons, F., Werker, J.. F., & Amano,
S. (2007). Unsupervised learning of vowel categories from infant-
directed speech. Proceedings of the National Academy of
les sciences, 104(33), 13273–13278. https://doi.org/10.1073/pnas
.0705369104, PubMed: 17664424
van den Oord, UN., Li, Y., & Vinyals, Ô. (2018). Representation
learning with contrastive predictive coding. ArXiv. https://arxiv
.org/abs/1807.03748v1
van Niekerk, B., Nortje, L., & Kamper, H. (2020). Vector-quantized
neural networks for acoustic unit discovery in the zerospeech
2020 challenge. In Proceedings of Interspeech (pp. 4836–4840).
International Speech Communication Association. https://doi.org
/10.21437/Interspeech.2020-1693
Versteegh, M., Thiollière, R., Schatz, T., Cao, X.-N., Anguera, X.,
Jansen, UN., & Dupoux, E. (2015). The zero resource speech
challenge 2015. In Proceedings of Interspeech (pp. 3169–3173).
International Speech Communication Association. https://doi.org
/10.21437/Interspeech.2015-638
Wanrooij, K., Boersma, P., & van Zuijen, T. L. (2014). Fast phonetic
learning occurs already in 2-to-3-month old infants: An ERP
étude. Frontiers in Psychology, 5(77), 1–12. https://est ce que je.org/10
.3389/fpsyg.2014.00077, PubMed: 24701203
Werker, J.. F., Byers-Heinlein, K., & Fennell, C. T. (2009). Bilingual
beginnings to learning words. Philosophical Transactions of the
Royal Society B, 364(1536), 3649–3663. https://est ce que je.org/10
.1098/rstb.2009.0105, PubMed: 19933138
Werker, J.. F., & Curtin, S. (2005). PRIMIR: A developmental frame-
work of infant speech processing. Language Learning and
Développement, 1(2), 197–234. https://doi.org/10.1080/15475441
.2005.9684216
Werker, J.. F., & Lalonde, C. E. (1988). Cross-language speech per-
ception: Initial capabilities and developmental change.
Developmental Psychology, 24(5), 672–683. https://est ce que je.org/10
.1037/0012-1649.24.5.672
Werker, J.. F., Pons, F., Dietrich, C., Kajikawa, S., Fais, L., & Amano,
S. (2007). Infant-directed speech supports phonetic category
learning in English and Japanese. Cognition, 103(1), 147–162.
https://doi.org/10.1016/j.cognition.2006.03.006, PubMed:
16707119
Werker, J.. F., & Tees, R.. C. (1984). Cross-language speech percep-
tion: Evidence for perceptual reorganization during the first year
de la vie. Infant Behavior and Development, 7(1), 49–63. https://est ce que je
.org/10.1016/S0163-6383(84)80022-3
Westermann, G., & Reck Miranda, E. (2004). A new model of sen-
sorimotor coupling in the development of speech. Brain and
Language, 89(2), 393–400. https://doi.org/10.1016/S0093-934X
(03)00345-6, PubMed: 15068923
Lequel, M., & Sundara, M.. (2019). Cue-shifting between acoustic
cues: Evidence for directional asymmetry. Journal of Phonetics,
75, 27–42. https://doi.org/10.1016/j.wocn.2019.04.002
Yeung, H. H., Chen, K. H., & Werker, J.. F. (2013). When does
native language input affect phonetic perception? The preco-
cious case of lexical tone. Journal of Memory and Language,
68(2), 123–139. https://doi.org/10.1016/j.jml.2012.09.004
Yeung, H. H., & Werker, J.. F. (2009). Learning words’ sounds
before learning how words sound: 9-month-olds use distinct
objects as cues to categorize speech information. Cognition,
113(2), 234–243. https://doi.org/10.1016/j.cognition.2009.08
.010, PubMed: 19765698
Ylinen, S., Uther, M., Latvala, UN., Vepsäläinen, S., Iverson, P.,
Akahane-Yamada, R., & Naätänen, R.. (2009). Training the brain
to weight speech cues differently: A study of Finnish second-
language users of English. Journal des neurosciences cognitives,
22(6), 1319–1332. https://doi.org/10.1162/jocn.2009.21272,
PubMed: 19445609
Yoshida, K. UN., Pons, F., Maye, J., & Werker, J.. F. (2010).
Distributional phonetic learning at 10 months of age. Infancy,
15(4), 420–433. https://doi.org/10.1111/j.1532-7078.2009
.00024.X, PubMed: 32693519
Yu, D., Deng, L., & Dahl, G. (2010, Décembre 10). Roles of
pre-training and fine-tuning in context-dependent DBN-HMMs
for real-world speech recognition [Paper presentation]. Neural
Information Processing Systems (NIPS) Workshop on Deep
Learning and Unsupervised Feature Learning, Whistler, BC.
Zevin, J.. D. (2012). A sensitive period for shibboleths: The long tail
and changing goals of speech perception over the course of
development. Developmental Psychobiology, 54(6), 632–642.
https://doi.org/10.1002/dev.20611, PubMed: 22714710
Zhao, J., Al-Aidroos, N., & Turk-Browne, N. B. (2013). Attention is
spontaneously biased toward regularities. Sciences psychologiques,
24(5), 667–677. https://doi.org/10.1177/0956797612460407,
PubMed: 23558552
Zlatin, M.. UN., & Koenigsknecht, R.. UN. (1975). Development of the
voicing contrast: Perception of stop consonants. Journal de
Speech and Hearing Research, 18(3), 541–553. https://doi.org
/10.1044/jshr.1803.541, PubMed: 1186163
OPEN MIND: Discoveries in Cognitive Science
131
je
D
o
w
n
o
un
d
e
d
F
r
o
m
h
t
t
p
:
/
/
d
je
r
e
c
t
.
m
je
t
.
/
e
d
toi
o
p
m
je
/
je
un
r
t
je
c
e
–
p
d
F
/
d
o
je
/
je
/
.
/
1
0
1
1
6
2
o
p
m
_
un
_
0
0
0
4
6
1
9
6
9
1
5
7
o
p
m
_
un
_
0
0
0
4
6
p
d
/
.
je
F
b
oui
g
toi
e
s
t
t
o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3
