DRAFT
Speech and Language Processing:
An introduction to natural language processing,
computational linguistics, and speech recognition.
Daniel Jurafsky & James H. Martin.
Copyright
c
circlecopyrt
2007, All rights reserved.
Draft of September 19, 2007.
Do not cite
without permission.
11
COMPUTATIONAL
PHONOLOGY
bidakupadotigolabubidakutupiropadotigolabutupirobidaku...
Word segmentation stimulus (Saffran et al., 1996a)
Recall from Ch. 7 that
phonology
is the area of linguistics that describes the sys
tematic way that sounds are differently realized in different environments, and how
this system of sounds is related to the rest of the grammar. This chapter introduces
computational phonology
, the use of computational models in phonological theory.
COMPUTATIONAL
PHONOLOGY
One focus of computational phonology is on computational models of phonological
representation, and on how to use phonological models to map from surface phonolog
ical forms to underlying phonological representation. Models in (noncomputational)
phonological theory are generative; the goal of the model is to represent how an under
lying form can generate a surface phonological form. In computation, we are generally
more interested in the alternative problem of
phonological parsing
; going from surface
form to underlying structure. One major tool for this task is the finitestate automaton,
which is employed in two families of models:
finitestate phonology
and
optimality
theory
.
A related kind of phonological parsing task is
syllabification
: the task of assigning
syllable structure to sequences of phones.
Besides its theoretical interest, syllabifi
cation turns out to be a useful practical tool in aspects of speech synthesis such as
pronunciation dictionary design. We therefore summarize a few practical algorithms
for syllabification.
Finally, we spend the remainder of the chapter on the key problem of how phono
logical and morphological representations can be learned.
11.1
F
INITE
S
TATE
P
HONOLOGY
Ch. 3 showed that spelling rules can be implemented by transducers. Phonological
rules can be implemented as transducers in the same way; indeed the original work
by Johnson (1972) and Kaplan and Kay (1981) on finitestate models was based on
phonological rules rather than spelling rules. There are a number of different models
of
computational phonology
that use finite automata in various ways to realize phono
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DRAFT
2
Chapter
11.
Computational Phonology
logical rules. We will describe the
twolevel morphology
of Koskenniemi (1983) first
mentioned in Ch. 3. Let’s begin with the intuition, by seeing the transducer in Fig. 11.1
which models the simplified flapping rule in (11.1):
/t/
→
[dx] /
´
V
V
(11.1)
2
1
0
3
other
other
V:@
V:@
V:@
V:@
t:dx
t
t
t
V:@
V:@
other
Figure 11.1
Transducer for English Flapping: ARPAbet “dx” indicates a flap, and the
“other” symbol means “any feasible pair not used elsewhere in the transducer”. “@” means
“any symbol not used elsewhere on any arc”.
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 Fall '11
 Staff
 Natural Language Processing, RA FT, computational phonology

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