Abstract 1 (Paola)
Title: A functional approach to the acquisition of sound contrasts solves the
inconvenience of separating underlying forms from perceptual strategies.
Abstract:
 There is a distinction between the mechanisms underlying perception and the
ones underlying production.
 I will argue for them to be not just processing devices (performance) but part
of knowledge (competence).
 Perceptual strategies are transferred, and so they belong to the grammar.
Example: If you ask a SS of English what the difference between /i/ and /I/ is,
they will say that it’s one of length; their judgment has been confirmed in
identification experiments (perception and production). Spanish cue
weighting: no difference in height equals “perceptual equivalence” “or singlecategory assimilation”, and so they use duration when differentiating between
the segments.
 Transfer depends on the grammar. If what people usually considered
performance could be transferred from the L1, is it still possible to call it
performance? If anything that you transfer has to be part of the grammar, then
we can’t call such things performance transfers but knowledge transfers.
 With this approach, we can account for category formation on the basis of
different perceptual strategies, and so it could be claimed that there is a
perception grammar that performs categorisation.
 There is a grammar of performance (in perception) and so there is no
distinction between competence and performance.
Abstract 2 (Paul)
Title: Extending the Gradual Learning Algorithm for style dependency
Session: Formalism and Functionalism in Linguistics
Abstract:
The GLA (Boersma & Hayes 2001) is able to learn the constraint ranking for a
grammar that shows free variation. At this talk, I will show how the Algorithm learns
a grammar that accounts for variation conditioned by style or register. While the task
of the learner in the original GLA was to adjust the ranking of each constraint, the
task of the learner in the extended GLA is not only to learn the ranking of these
constraints but also the style dependency of each constraint.
Thus, if there are N constraints the learner will have adjust the values of 2N
quantities. For the learner to be able to achieve this she will have to know the adult
speaker’s surface form, the adult speaker’s style or register, and, if faithfulness
constraints are concerned, the underlying form. I will show how the extending GLA
works on the following examples: xx; pied-piping vs. preposition stranding in
English.
The conclusion is that examples of style dependant variation found in the
literature can be handled well with the extended GLA.
Note: Paul will produce another abstract for GALA about how to solve subset and
superset problems in language acquisition.
Abstract 3 (Paola &Paul)
TITLE: The Gradual Learning Algorithm predicts the development of first and second
language phonological contrasts
SESSION: formalism and functionalism in Linguistics
AUTHORS: Paul Boersma & Paola Escudero
AFFILIATIONS: University of Amsterdam & University of Reading
ADDRESS FOR NOTIFICATION:
Paul Boersma
Institute of Phonetic Sciences
Herengracht 338
NL-1016 CG Amsterdam
The Netherlands
PHONE: +31-24-3581839, +44-118-9755796
EMAIL: paul.boersma@hum.uva.nl, p.r.escudero@reading.ac.uk
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The Gradual Learning Algorithm predicts the development of first and second
language phonological contrasts
Acoustically, the English vowels /I/ and /i/ differ in vowel height (first formant) and
length (duration). One would expect, therefore, that listeners rely on both cues when
distinguishing between these vowels. Escudero (to appear a, b), however, found that
Standard Scottish English listeners rely almost exclusively on height, while Southern
English speakers rely on both durational and spectral cues. Escudero hypothesised
that this perceptual variation is caused by regional variation in production. In a
production experiment, we found that in Scottish English, /i/ is much shorter than in
Southern English, so that the actually produced durations of /I/ and /i/ overlap
appreciably. Conversely, Southern English /I/ turned out to be much higher than
Scottish English /I/, i.e. spectrally closer to /i/. To minimize the probability of
miscomprehending the speaker's utterance, the Scottish listener must learn to rely
mainly on height, the Southern English listener more on length.
Our hypothesis, then, is that optimal cue reliance depends on cue reliability.
Boersma's (1998) three-grammar model of phonological acquisition, which
implements a separate perception grammar within an Optimality-Theoretic
framework, predicts exactly this hypothesised relation when a general Gradual
Learning Algorithm (Boersma & Hayes, to appear) is applied to the perception
grammar (Boersma 1997). This grammar consists of a large number of constraints
such as “an F1 of 350 Hz is not an /I/” and “a duration of 80 ms is not an /i/”, for evry
value of F1 and duration, and for both vowels. Thus, if both the spectral and
durational continua are divided into seven discrete regions, the grammar contains 28
constraints.
We let this algorithm simulate the behaviour of two virtual babies, little Elspeth and
little Liz, who are brought up in Scotland and Southern England, respectively. We
will show how both of these listeners start with the same perception grammar, in
which the constraints are ranked at the same level. The learners then repeatedly "hear"
words with /I/ and /i/, appropriately distributed with respect to height and length
according to their language environment. Every time little Elspeth or little Liz
miscomprehends a word, she reranks some constraints in her perception grammar.
Gradually, Elspeth comes to rely almost exclusively on height, whereas Liz comes to
rely on both duration and height, thus achieving adult-like minimisation of perceptual
confusion.
We also let the algorithm simulate the behaviour of two adult Spanish speakers, Isabel
and Carmen, who move to Scotland and Southern England, respectively. The results
show that a listener with an established perception grammar will also rerank the
constraints and can ultimately attain native-like perception.
Consequently, the formal three-grammar model of Functional Phonology, together
with the Gradual Learning Algorithm, accounts for the acquisition of an effective
perception of sound contrasts.
REFERENCES
Boersma, Paul (1997): How we learn variation, optionality, and probability.
Proceedings of the Institute of Phonetic Sciences, University of Amsterdam, 21: 4358. [= ch.15 of Boersma 1998]
Boersma, Paul (1998): Functional Phonology. Doctoral thesis, University of
Amsterdam. The Hague: Holland Academic Graphics.
Boersma, Paul & Bruce Hayes (to appear): Empirical tests of the Gradual Learning
Algorithm. Linguistic Inquiry, Winter 2001.
Escudero, Paola (to appear a): The perception of English vowel contrasts: acoustic
cue reliance in the development of new contrasts. In Jonathan Leather & Allan James
(eds.) Proceedings of the 4th International Symposium on the Acquisition of SecondLanguage Speech, New Sounds 2000, University of Amsterdam.
Escudero, Paola (to appear b): Input, L1, and universal strategies in the development
of new contrasts: the L2 relative weighting of acoustic information. Paper presented at
the 25th Annual Boston University Conference on Language Development,
November 2000.