PHONY: A Heuristic Phonological Analyzer* 
Lee A. Becket 
Indiana University 
DOMAIN AND TASK 
PHONY is a program to do phonological 
analysis. Within the generative model of 
grammar the function of the phonological 
component is to assign a phonetic 
representation to an utterance by modifying 
the underlying representations (URs) of its 
constituent morphemes. Morphemes are the 
minimal meaning units of language, i.e. the 
smallest units in the expression system 
which can be correlated with any part of the 
content system, e.g. un+tir+ing+ly. URs are 
abstract entities which contain the 
idiosyncratic information about 
pronounciations of morphemes. 
(1) 
PHONOLOGICAL 
Underlying COMPONENT Phonetic 
Representations ............ > Representations 
(URs) (rules) 
Phonological analysis attempts to determine 
the nature of the URs and to discover the 
general principles or rules that relate them 
to the phonetic representations. 
(2) 
URs 
Pronounciations PHONY 
(phonological anal 
Rules 
The input to PHONY are pronounciations of 
words and phrases upon which a preliminary 
morphological analysis has been completed. 
They have been divided into morphemes, and 
different instances of the same morpheme 
have been associated. These are represented 
as strings of phonetic symbols including 
morpheme- and word-boundaries. Indices are 
used to associate various instances of the 
same morpheme. 
(3) 
# i s a r a p # # 1 s a r a b + 2 d a # 
# 1 s a r a v + 3 u # # 1 s a rav + 4 e # 
# 5 a d + 6 a # # 5 a t # ,,, 
The output of PHONY is a set of phonological 
rules or regularities in the data, as well 
as a set of 'underlying representations' 
for the morphemes. The phonological rules 
generate the various pronounciations of the 
morphemes from their underlying 
representations. 
*This research was supported in part by 
National Science Foundation grant 
number MCS 81-02291. 
REPRESENTATION 
In Generative Phonology sounds are 
represented as matrices of feature 
specifications, the phonetic symbols being a 
shorthand for these matrices. (4) 
- syllabic 
+ consonanta~ 
- continuant 
+ voice 
- nasal 
+ anterior 
+ coronal 
The set of 'distinctive features' proposed 
by Chomsky and Halle \[2\] were claimed to be 
sufficient to distinguish the sounds in any 
language. Further these features were all 
claimed to have two values; the feature was 
either present or absent. There has been a 
fair aunount of dispute about the specific 
features, and several additional ones have 
been proposed, e.g. gravity, advanced tongue 
root. There has also been considerable 
dispute about whether the features are all 
binary. Nevertheless most phonologists use 
the original binary features, often with a 
few additional ones. Phonological rules are 
operations upon sets of these feature 
matrices by which feature specifications are 
assigned to the matrix when it appears in a 
certain context. The rule expressed (in 
shorthand) normally as 
(e) 
S -> S /ji (read s becomes s in position 
immediately before i) 
would be expressed as follows using feature 
matrices. 
(7) E 
coronal anterio l syllabi  
anterior I~ high 2/-" high I 
strident ~ back J 
The representation provides a language in 
which to express hypotheses. The task is to 
find statements in this language to express 
the data. Thus the representation 
implicitly defines the search space. The 
search space is restricted by the following 
constraint on the 'distance' between a UR 
and its pronounciations. Every feature 
specification in the UR must be present in a 
'corresponding' segment in at least one of 
the phonetic forms. Consider, for example, 
morpheme i from (3) above: it ham three 
pronounciations \[sarap\], \[sarab\], \[sarav\]. 
23 
This constraint restricts its possible URs 
to /sarap/, /sarah/, /sarav/, /saraf/. 
Even If\] does not appear in any of the 
pronouciations of this morpheme, its 
+continuant specification occurs in Iv\] and 
its -voice specification occurs in \[p\]; its 
other feature specifications are common to 
\[p\], Cb\], Iv\]. This constraint is weaker 
than the "strong alternation condition" (cf. 
\[4\]), which would restrict the final UR 
segment to be /p/, /b/, or /V/o The term 
"alternation" will be important of the 
discussion below; here \[p\] vs. \[b\] vs. Iv\] 
is an alternation. 
THE PROBLEM OF MULTIPLE SOLUTIONS 
It should be pointed out that most often 
several sets of combinations of underlying 
representations and phonological rules can 
be used to derive the same pronounciations. 
This could happen in several ways. It could 
be unclear what the UR is, and different URs 
together winh different rules could derive 
that same pronounciatons, i.e. the 
directionality of the rule could be unclear. 
Consider morpheme 5 from (3) above: 
(8) 
Pronounciations: #ad÷a# #at# 
Solution I: UR /ad/ & Rule d -, t / # 
Solution 2: UR /at/ & Rule t -> d / a a 
The symbol # represents a word boundary, and 
the symbol + represents a morpheme boundary, 
The difference in the pronounciation of the 
last segment of this morpheme, d vs. t, is 
called an alternation. Given this 
alternation, one could make two hypotheses. 
One could hypothesize that the UR is /ad/ 
and that there is a rule which changes d to 
t when it occurs at the end of a word, or 
one could hypothesize that the UR is /at/ 
and that there is a rule which changes t to 
d between a's. Also some phenomena could be 
explained by a single more general rule or 
by several more specific rules. 
Generally, there are two approaches that 
could be taken to deal with the problem of 
multiple possible solutions. One could 
attempt to impose restrictions on what could 
constitute a valid solution, or one could 
use an evaluation procedure to decide in 
cases of multiple possible solutions. One 
could also use both of these approaches; in 
which case the more restriction, the less 
evaluation is necessary. An original single 
evaluation criterion - 'simplicity', as 
manifested in the number of feature 
specifications used - has not proved 
workable. ALso no particular proposed 
restrictions have been embraced by the v~st 
majority of phonologists. 
Individual phonologists are generally guided 
in their evaluations of solutions, i.e. sets 
of rules and URs, by various criteria. The 
weighting of these criteria is left open. 
In this connection the 'codifying function' 
of the development of expert systems is 
particulary relevant, i.e. in order to be 
put into a program the criteria must be 
formalized and weighted.j5\] Although it has 
sometimes been claimed that no set of 
discovery procedures can be sufficient tO 
produce phonological analyses, this program 
is intended to demonstrate the feasibility 
of a procedural definition of the theory. 
The three most widely used criteria and the 
manner in which they are embedded in PHONY 
will now be discussed. 
Phonological Predictability 
This involves the preference of solutions 
based phonological environment rather than 
to those in which reference is made to 
morphological or lexical categories or 
involving the division of the lexicon into 
arbitrary classes. In other words, in doing 
phonological analysis the categories or 
meanings of morphemes will not be 
considered, unless no solution can be found 
based on just the sounds or sound sequences 
involved. This criterion is embodied in 
PHONY, since no information about morpholog- 
ical or syntactic categories is available to 
PHONY. If PHONY cannot handle an 
alternation by reference to phonological 
environment, it will return that this is an 
'interesting case'. The ability to identify 
the *interesting cases' is a most valuable 
one, since these are often the cases that 
lead to theory modification. It should be 
mentioned that PHONY could readily be 
extended (Extension I) to handle a certain 
range of syntactically or morphologically 
triggered phonological rules. This would 
involve including in the input information 
about syntactic category, and, where 
relevant, morphological category of the 
constituent morphemes. This informaton 
would be ignored unless PHONY was unable to 
produce a solution, i.e. would have returned 
"interesting cases"'. It would then search 
for generalizations based on these 
categories. 
Naturalness 
This involves the use of knoweldge about 
which proceeses are 'natural' to decide 
between alternate solutions, i.e. solutions 
involving natural processes are preferred. 
A process found in many languages is judged 
to be 'natural'. Although natural processes 
are often phonetically plausible, this is 
not always the case. It should be mentioned 
that not only is 'naturalness' an arbiter in 
case of several possible solutions, but it 
is also a heuristic to lead the investigator 
to plausible hypotheses which he can pursue. 
PHONY contains a catalogue of natural 
processes. When an alternation looks as if 
it might be the result of one of these 
processes, the entire input corpus of 
strings is tested to see.if this hypothesis 
is valid. 
Simplicity 
'Simplicity' was mentioned above, while it 
is no longer the only criterion, it is still 
a primary one. It is reflected in PHONY in 
a series of attempts to make rules more 
general, i.e. combine several hypothesized 
rules into a single hypothesized rule. The 
more general rules require fewer feature 
specifications. Also the smaller number of 
24 
rules can lead to a reduced number of 
feature specifications. 
The various proposed constraints on what can 
be valid solutions generally would correlate 
with the differences in the testing process 
of PHONY. Most of these involve differences 
in allowable orderings of rules (e.g. 
'unrestricted extrinsic ordering', 'free 
reapplication', 'direct mapping'; cf. \[3\]). 
At present PHONY's testing process involves 
checking if hypothesized rules hold, i.e. do 
not have counterexemples, in the phonetic 
representations (such a criterion disallows 
opacity of type l; of. \[4\]). PHONY could be 
extended (Extension 2) to allow the user to 
choose from several of the proposed 
constraints. This would involve using 
different testing functions. This extension 
would allow analyses of the same data under 
different constraints to easily be compared. 
Additionally, new constraints could be added 
and tested. 
STRUCTURE OF PHONY 
PHONY can be divided into three major parts~ 
ALTFINDER, NATMATCH, and RULERED. 
ALTFINDER 
ALTFINDER takes the input sting of phonetic 
symbols and indices indicating instances of 
the same morpheme, as in (3), and returns 
for each morpheme in turn a representation 
including the non-alternating segments and 
list of alternations with the contexts in 
which each alternant occurs, for example, 
for morpheme I, as in (9). 
(9) 
sara p ~ b -~ v 
# sarap # # sarah + da # # sarav + u # 
# sarav ÷ e # 
This process involves comparing in turn each 
instance of a given key morpheme with the 
current hypothesized underlying 
representation for that morpheme, and for 
each case of alternation storing in N groups 
the different context strings in which the N 
alternants occur. The comparison is 
complicated by the common processes of 
epenthesis (insertion of a segment) and 
elision (deletion of a segment), and 
occasionally by the much more rarely 
occurring methathesis (interchange in the 
positions of two segments). These processes 
are illustrated in (10). 
(10) 
Given UR / t a r i s k /, 
Epenthesis ~ -> a \[trisk\]\[tarisak\] would .~nv°Ive Elision a -> 
\[tariks\] " Methathesis sk -> ks 
Therefore in cases where the segments being 
compared are not identical it is necessary 
to ascertain whether they are variants of a 
single underlying segment or one of these 
processes has applied. The possibilities are 
illustrated in (11). 
(ii) 
Given two pronounciations of the same 
morpheme 
\[ A B C . . . \] where A is associated with D 
\[ D E F . . . \] and B is not identical to E, 
There are four possible relationships: 
Bi c... A\B\cl "'" 
D E F ... D E F ... 
A B C ... A B C ... 
The criteria used to decide between these 
relationships are (a) degree of similarity 
in each of the conceivable associations, and 
(b) a measure of the similarity of the rest 
of the strings for each of the conceivable 
associations. 
ALTFINDER yields a list of alternations 
based on segments, as in (9). This is then 
converted into a list of alternations based 
on features. 
(12) 
P p-contexts b v b-contexts v-contexts 
,U, VOICE ÷ 
b-contexts & v-contexts p-contexts 
CONTINUANT + 
v-contexts b-contexts & p-contexts 
Since every one of the alternations in the 
former must differ by at least one feature, 
the new list must contain as many 
alternations and normally contains more 
alternations, Where previously for each 
alternation in a segment there was a list of 
strings where each alternant occurred, now 
for each alternation in a feature there are 
two lists - one with the strings where a 
positive value for that feature occurred and 
the other where a negative value occurred. 
It should be noted that the elements of 
these lists, i.e. strings, together with the 
feature alternating, its value, and an 
indication of which segment in the string 
contains the feature, are all potentially 
rules. They bear the same information as 
standard phonological rules. Compare the 
representations in (13); these are for the 
alternations in morpheme 5 in (3). 
25 
(13) 
# a d + a # # a t # 
i I I 1 
0 I 0 0 0 
0 0 0 0 0 
0 0 0 0 0 
0 0 0 0 0 
0 1 0 1 0 0 l 0 
0 O 1 0 0 0 0 0 1 0 
0 1 0 0 l 0 0 l 0 0 
0 0 0 0 0 0 0 0 0 0 
0 1 0 0 l 0 0 1 0 0 
0 1 0 0 1 0 0 i 0 0 
0 0 1 0 0 0 0 0 I O" 
0 0 1 0 0 0 0 0 1 0 
0 1 i 0 1 0 VOICE 0 I 0 0 
0 i 0 0 1 0 0 1 0 0 
0 0 0 0 0 0 0 0 0 0 
0 0 0 0 0 0 0 0 0 0 
0 0 0 0 0 0 0 0 0 0 
0 0 0 0 0 0 0 0 0 0 
to the rules t -> d / # a + a # d -> t / # a 
# , i.e. respectively, one can't pronounce t 
in the environment # a + a # but rather must 
pronounce d, and one can't pronounce d in 
the environment # a # but rather must 
pronounce t. The latter rule and the second 
representation (both without the initial 
two segments - in the interests of space) in 
(13) are juxtaposed in (14). 
(14) 
1000011000000 1000000000000000 
D-> T/ # 
It is often the case that one or both of 
these potential 'rules' will be valid, i.e. 
would be generalizations that would hold 
over the pronounciations represented in the 
input. These 'rules' would, however, be 
much less general than those which are found 
in phonological analyses. It is assumed 
that speaker/hearer/language learners can 
and do generalize from these specific cases 
to form more general rules. If this were 
not the case how could speakers correctly 
pronounce morphemes in new environments. 
Within the theory the criterion of 
simplicity is sensitive to these 
generalizations in that such generalizations 
reduce the number of feature specifi- 
cations. Within PHONY the preference for 
more general rules is manifested by 
continually trying to generate and test more 
general rules resulting from the coalescing 
or combining of two or more specific rules. 
Recall that the representation of the 
segments involved a feature matrix with 
positive or negative specifications for each 
feature. In order to generate more general 
rules this repuesentation is modified to two 
matrices for each segment - one representing 
those features which must be positive in the 
environment and the other for those features 
which must be negative. The generalization 
process involves taking the 'greatest common 
denominator' (GCD) of the positive and 
negative values of the segments of the 
environments of two separate 'rules'. In the 
interests of space an abbreviated example of 
the GCD operation is given in (15). 
(15) 
+ . ÷ -- ÷ ÷ - + -- 
SYLL i 0 0 1 i 0 0 i 1 0 
VOICE i 0 l 0 1 0 0 i i 0 
HIGH 0 1 1 0 l 0 h 1 0 i 0 
/ 
+ -- ÷ - 
~voIcEI VOICEHIGH 01 00 11 00 ~ \[-S~L\]-'C÷HIGH\]/ ~HIGH\] m ~ 
The GCD operation has generated a more 
general rule. If the original two rules are 
a manifestation of a more general rule, the 
generalized rule must not involve or make 
reference to the the initial segment of the 
former rule. Notice also that in the GCD 
the VOICE feature does not have to be 
positive or negative; if the two original 
rules are a manifestation of a single rule 
the specification of the VOICE feature in 
the alternating segment must not be 
relevant. 
NATMATCH 
After the alternations in terms of segments 
that were output by ALTFINDER have been 
changed into alternations in terms of 
features (12) and after these have been 
transformed from single matrices into double 
matrices, the resulting "rules" are sent to 
NATMATCH. NATMATCH compares these "rules" 
with the data base of common phonological 
processes. This involves pattern matching. 
If a match occurs the entire input corpus is 
tested to find out if it can be established 
whether this rule or constraint is valid for 
this language. If Extension 2 were 
implemented, this testing process would 
differ for the different versions of the 
theory. If the validity can be established, 
the underlying representations for the 
morpheme is adjusted and the rule is added 
to the list of established rules. Common 
processes in the data base are organized by 
the feature which is alternating, and among 
those processes involving the alternation of 
a given feature the most common process is 
listed and thus tested first. If it can be 
shown to be valid, it is added to a list of 
established rules. It should be mentioned 
that ALTFINDER makes use of this list, and 
if an alternation that it discovers can be 
handled by an established rule, the 
tentative underlying representation is so 
adjusted and the alternation need not be 
passed on to the rest of the program. If 
within NATMATCH no matches are found in the 
data base or if the validity of the matches 
cannot be established, the alternation is 
added to the list of those as yet not 
accounted for. 
RULERED 
RULERED takes the generated "rules" that 
have not been established. It establishes 
which of these are valid and takes GCDs to 
generalize these as much as possible. This 
is done by going through all the rules 
involving a certain feature and generating 
the minimal number of equivalence classes of 
"rules" and combined (GCDed) "rules" which 
26 
are valid. The resulting generalized rules 
have the largest matrices, i.e. the largest 
set of feature specification@, which all the 
forms undergoing these rules have in common. 
However, the elimination of some of these 
features specification might still result in 
valid rules. The rules with minimal 
matrices, i.e. minimal number of feature 
specifications (recall the "simplicity" 
criterion), might be termed lowest commmon 
denominators (LCDs). These are produced by 
attempting in turn to eliminate each segment 
in GCDed rule; the new rule is generated and 
tested, and if valid the segment is out, 
otherwise it remains. Then an attempt is 
made to eliminate in turn each feature 
specification in the remaining segments, 
again generate and test. Finally, all the 
established rules are combined, where 
possible, according to the many abbreviatory 
conventions of Generative Phonology (cf. 
\[2\]). This is done on the basis of the 
formal properties of the rules. For example, 
if two generated rules are identical except 
that one has an additional segment not 
present in the other, these can be into a 
single rule; parentheses allow the inclusion 
of optional segments in the environment of a 
rule. In addition, all the rules generated 
above involve a change of only a single 
feature specification. If there are several 
rules which are identical except that a 
different feature specification is changed, 
i.e. the two changes occur in the same 
environment, they can be combined into a 
single rule: in this particular environment 
both specifications change. 
DISCUSSION 
PHONY is a learning program. It is 
discovering the general principles or rules 
governing pronounciation in a language. As 
such it can be said to be learning some 
aspect of a language. PHONY can be thought 
of either independently or as a part of a 
larger system designed to learn a language. 
In the latter context PHONY could help in 
deciding between ambiguous morphological 
divisions. In addition, PHONY could be used 
in adjusting, fine-tuning heuristics for a 
morphological analyzer. PHONY would act as 
a "critic" in such a system (cf. \[i\]). Two 
sets of heuristics might lead to different 
morphological analyses, which might each be 
input to PHONY~ if one input lead to 
analysis that had no "interesting cases", 
i.e. problems, while the other did, the set 
of heuristics leading to the former analysis 
would be supported. 
Independently PHONY is an expert system. It 
provides a procedural definition of 
phonological theory. Because of this, it 
could be useful to someone desiring to learn 
phonological theory. It could also be of 
use to working phonologists. In addition to 
producing the analyses, it also isolates the 
'interesting cases', e.g. morphologically 
triggered rules. With Extension i it could 
also be used to compare various versions of 
the theory and to test the the effects of 
new modifications of the theory. 
It should be emphasized that at 
present PHONY is ~ bare program. It is 
hoped that it is sufficient to demonstrate 
the feasability and worth of the endeavor. 
It presents a basic approach: contexts in 
with alternating segments are transformed 
into hypothesized "rules", these can be 
combined via the GCD operation, further 
simplified to LCDs, and then again combined 
according to the abbreviatory conventions. 
There is a "grinding" quality to this 
process. Phonologists only resort to a 
similar grind, when all their heuristics 
have led to deadends. The only heuristic 
presently incorporated in PHONY is the 
comparison to a list of natural processes; 
this allows a tremendous shortcut in the 
search More heuristics obviously could be 
added to PHONY. 
It would also be possible for a 
METAPHONY to find heuristics to be to be 
used by PHONY. (Possible decision criteria 
to be used in evaluating differing sets of 
heuristics could be the number of tests of 
the input corpusand the number of 
"interesting cases".) These heuristics could 
improve efficiency of PHONY by obviating 
much of the "grinding" process. At the same 
time METAPHONY could also be making 
discoveries about phonologies of natural 
languages in general. For example, in the 
process of generating LCDs instead of going 
segment by segment and feature by feature, 
METAPHONY could acquire and incorporate in 
PHONY knOwledge about what aspects of 
pronounciation are not/rarely pertinent to 
rules affecting a certain feature. 
REFERENCES 
i. Buchanan, B.G., T.M. Mitchell, R.G. 
Smitch, C.R. Johnson, Jr. 1979. Models of 
learning systems. Encyclopedia of Computer 
Science and Technology. J. Belzer, A. 
Holtzman, A. Kent (Eds.). New York: Marcel 
Dekker, Inc. Vol 3, pp 24-51. 
2. Chomsky, N. and M. Halle. 1968. The 
Sound Pattern of English. New York: Harper 
and Row. 
3. Kenstowicz, M. and C. Kisseberth. 1977. 
Topics in Phonological Theory. New York: 
Academic Press. 
4. Kiparsky, P. 1968. How abstract is 
phonology? In O. Fujimura (Ed.), Three 
Dimensions in Linguistic Theory. 1973. 
Tokyo: TEC. 
5. Michie. D. 1980. Knowledge-based 
systems. UIUCDCSR-80-1001 and UILU-Eng 
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