Stress AJ~p,aMm is Lett~ m Se,,,td Rats fer Speech Sy~ 
Kenneth Church 
AT&T Boll Laboratories 
Abreact 
This paper will discuss how to determine word stress from spelling. 
Stress assignment is a well-established weak point for many speech 
synthesizers because stress dependencies cannot be determined locally. 
It is impossible to determine the stress of a word by looking through a 
five or six character window, as many speech synthesizers do. Well- 
known examples such as degrade / dbgradl, tion and tMegraph / 
telegraph5 demonstrate that stress dependencies can span over two and 
three syllables. This paper will pre~nt a principled framework for 
dealing with these long distance dependencies. Stress assignment will 
be formulated in terms of Waltz' style constraint propagation with four 
sources of constraints: (1) syllable weight. (2) part of speech. (3) 
morphology and (4) etymology. Syllable weight is perhaps the most 
interesting, and will be the main focus of this paper. Most of what 
follows has been implemented. 
I. Back~e,,sd 
A speech synthesizer is a machine that inputs a text stream and 
outputs an accoustic signal. One small piece of this problem will be 
discussed here: words -- phonemes. The resulting phonemes are then 
mapped into a sequence of Ipe dyads which are combined with 
duration and pitch information to produce speech. 
text -- intonation phrases -- words 
phonemes -- Ipc dyads + prosody -- accousti¢ -~ 
There are two general approaches to word -- phonemes: 
• Dictionary Lookup 
• Letter to Sound (i.e.. sound the word out from basic principles) 
Both approaches have their advantages and disadvantages; the 
dictionary approach fails for unknown words (e.g.. proper nouns) and 
the letter to sound approach fails when the word doesn't follow the 
rules, which happens all too often in English. Most speech synthesizers 
adopt a hybrid strategy, using the dictionary when appropriate and 
letter to sound for the rest. 
Some people have suggested to me that modern speech synthesizers 
should do away with letter to sound rules now that memory prices are 
dropping so low that it ought to be practical these days to put every 
word of English into a tiny box. Actually memory prices are still a 
major factor in the cost of a machine. But more seriously, it is not 
possible to completely do away with letter to sound rules because it is 
not possible to enumerate all of the words of English. A typical 
college dictionary of 50,000 hcadwords will account for about 93% of a 
typical newspaper text. The bulk of the unknown words are proper 
flOUfl-q. 
The difficulty with pmpor nouns h demonstrated by the table below 
which compares the Brown Corpus with the surnames in the Kansas 
City Telephone Book. The table answers the question: how much of 
each corpus would be covered by a dictionary of n words? Thus the 
first line shows that a dictionary of 2000 words would cover 68% of the 
Brown Corpus, and a dictionary of 2000 names would cover only 46% 
of the Kansas City Telephone Book. It should be clear from the table 
that a dictionary of surnames must be much targar than a typical 
college dictionary ('20,000 entries). Moreover. it would be a lot of 
work to consu'u~ such a dictionary since there are no existing 
computer readable dictionaries for surnames. 
Size of Brown Size of 
Word Dictionary Corpus Name Diczionary 
2000 68% 2000 
4000 78% 4000 
6000 83% 6000 
8000 86% 8000 
lO000 89% 10000 
12000 91% 12000 
14000 92% 14000 
16000 94% 16ooo 
! 800O 95% 18000 
20000 95% 20000 
22000 96% 22000 
24000 97% 24000 
26000 97% 26000 
28000 98% 28000 
30000 98% 30000 
32000 98% 32000 
34000 99% 34000 
36000 99% 36000 
38000 99% 38000 
40(3O0 99% 
Kansas 
46% 
57% 
63% 
68% 
72% 
75% 
77% 
79% 
81% 
83% 
84% 
86% 
87% 
88% 
89% 
9O% 
91% 
91% 
92% 
93% 
246 
Actually, this table overestimates the effectivene~ of the dictionary, 
for practical applications. A fair test would not use the same corpus 
for both selecting the words to go into the dictionary and for testing 
the coverage. The scores reported here were computed post hoc, a 
classic statistical error, l tried a more fair test, where a dictionary of 
43777 words (the entire Brown Corpus) was tested against a corpus of 
10687 words selected from the AP news wire. The results showed 96% 
coverage, which is slightly lower (as expected) than the 99% figure 
reported in the table for a 40000 dictionary. 
For names, the facts are much more striking as demonstrated in the 
following table which teats name lists of various sizes against the Bell 
Laboratories phone book. (As above, the name lists were gathered 
from the Kansas City Telephone Book.)* 
Size of Word List Coverage of Test Corpus 
(Kansas) (Befl Labs) 
2000 
400O 
60OO 
8000 
I0000 
20000 
4000O 
50000 
6000O 
9OOOO 
0.496 
0.543 
0.562 
0.571 
0.577 
0.589 
0.595 
0.596 
0.596 
0.597 
Note that the asymptote of 60% coverage is quickly reached after only 
about 5000-1000 words, su88estiog (a) that the dictionary appnxtch 
may only be suitable for the 5000 to 1000 mint frequent names 
because larger dictionaries yield only negligible improvements in 
performance, and (b) that the dictionary approach has an inherent 
limitation on coverage of about 60%. To increase the coverage beyond 
this, it is probably neceqsary to apply alternative methods such as letter 
to sound rules. 
Over the past year l have been developing a set of letter to sound rules 
as part of a larger speech synthesis project currently underway at 
Murray Hill. Only one small piece of my letter to sound rules, 
orthography ~ stress, will be discussed here. The output streu 
assignment is then used to condition a number of rules such as 
palatalization in the mapping from letters to phonemes. 
2. we/ght as ~ i,termt~tm ~ of Relm~mmutm 
Intuitively, stre~s dependencies come in two flavors: (a) those that 
apply locally within a syllable, and (b) throe that apply globally 
between syllables. Syllable weight is an attempt to represent the local 
stress constraints. Syllables are marked either heavy or light, 
depending only on the local 'shape' (e.g., vowel length and number of 
Ix~t-vocalic consonants). Heavy syllables are more likely to be 
• Admittedly. this teat is somewhat unfair to the dictionary appma©h sinca: thu ethnic 
mzxture in gamuut City is very differeat from that found here at Bell t.aboflltot~ 
stressed than light syllables, though the actual outcome depends upon 
contextual constraints, such as the English main stress rule, which will 
be d~ shortly. 
The notion of weight is derived from Chomsky and Halle's notion of 
strong and weak clusters \[Chonuky and Halle\] (SPE). In 
phonological theory, weight is used as an intermediate level of 
representation between the input underlying phonological 
representation and the output stress aaignment. In a similar fashion, \[ 
will use weight as an intermediate level of representation between the 
input orthography and the output strew. The orthography -- stress 
problem will be split into two subproblems: 
• Orthography -- Weight 
• Weight ~ Stress 
3. What is Sy~ Weight: 
Weight is a binary feature (Heavy or Light) assigned to each syllable. 
The final syllables of the verbs obey, maintain, erase, torment. 
collapse, and exhaust arc heavy because they end in a long vowel or 
two consonants, in constrast, the final syllables of develop, astonish. 
edit. consider, and promise are light because they end in a short vowel 
and at moat one consonant. More precisely, to compute the weight of 
a syllable from the underlying phonological representation, strip off. the 
final consonant and then pane the word into syllables (assigning 
¢omommts to the right when there is ambiguity). 
owK y Weight Rea.~oa heavy final syllable long vowel 
tor-men heavy final syllable closed syllable 
diy-ve-lo light final syllable open syllable & short vowel 
Then. if the syllable is clo~ (i.e., ends in a consonant as in tor.men) 
or if the vowel is marked underiyingly long (as in ow.bey), the syllable 
is marked heavy. Otherwise, the syllable ends in an open short vowel 
and it is marked light. Determining syllable weight from the 
orthography is considerably more difficult than from the underlying 
phonological form. I will return to this question shortly. 
4. we/slt -- Stnm 
Global stress assignment rules apply off" the weight representation. For 
example, the main stress rule of English says that verbs have final 
stress if the final syllable is heavy syllable (e.g., obey), and penultimate 
stress if the final syllable light syllable (e.g., develop). The main stress 
rule works similarly for nouns, except that the final syllable is ignored 
(extrametrical \[Hayes\]). Thus, nouns have penultimate stress if the 
penultimate syllable is heavy (e.g, aroma) and antipenultimate stress 
if the penultimate syllable is light (e.g., cinema). 
£x~l~ Pesmilimte Wei~lst R~ 
heavy long vowel 
verr6nda heavy closed syllable 
cinema light open syllabic & short vowel 
247 
Adjectives stress just like verbs except suffixes are ignored 
(extrametrical). Thus monomorphemic adjectives such as diacr~et, 
robfist and cbmmon stress just like verbs (the final syllable is stressed 
if it is heavy and otherwise the penultimate syllable is stress) whereas 
adjectives with single syllable suffixes such as -al, -oas. -ant, -ent and 
-ire follow the same pattern as regular nouns \[Hayes, p. 242\]. 
Stress Pattera of Suffixed Adjectives 
Light Penultimate Hury Peaaidmate Heavy Pmultimale 
municipal adjectival frat&'nai 
magn~minous desirous trem~ndoas 
significant clairv6yant relfictant 
innocent complY, cent dep6'ndent 
primitive condficive exp~-nsive 
S. SWeat's WeiOt Table 
A large number of phonological studies (e.g., \[Chomsky and HalleL 
\[Liberman and PrineeL \[Hayes\]) outline a deterministic procedure for 
assigning stress from the weight representation and the number of 
extrametrical syllables (1 for nouns, 0 for verbs). A version of this 
procedure was implemented by Richard Sproat last summer. 
For efficiency purposes. Sproat's program was compiled into a table,, 
which associated each possible input with the appropriate stress 
pattern. 
Sweat's Weight Table 
Part of Speech 
Weight 
Verb Noun 
H .I I 
L l I 
HH 31 I0 
HL I0 I0 
LH 01 I0 1 
LL I0 I I0 1 
HHH 103 \] 3101 
HHL 310 I 310 
HLH 103 1(30 
HLL 310 10O 
LHH 103 010 
LHL 010 010 
LLH I03 10O 
LLL 010 100 
etc. 
Note that the table is extremely small. Assuming that words have up 
N 
to N syllables and up to E extrametrical syllables, there are E~2 ~ 
possible inputs. For E - 2 and N - 8, the table has only 1020 entries, 
which is not unreasonable. 
6. Amlolff with Walt-' Comtndat Prolmptiea Paradigm 
Recall that Waltz was the first to showed how contraints could be used 
effectively in his program that analyzed line drawings in order to 
separate the figure from the ground and to distinguish concave edges 
from convex ones. He first assigned each line a convex label (+), a 
concave label (-) or a boundary label (<, >), using only ~ocal 
information. If the local information was ambiguous, he would assign 
a line two or more labels. Waltz then took advantage of the 
constraints impmed where multiple lines come together at a common 
vertex. One would think th~ t there ought to be 42 ways to label a 
vertex of two lines and 4 '~ ways to label a vertex of three lines and so 
on. By this argument, there ought to be 208 ways to label a vertex. 
But Waltz noted that there were only 18 vetex labelings that were 
consistent with certain reasonable assumptions about the physical 
world. Because the inventory of possible labelings was so small, he 
could disambiguate lines with multiple assignments by checking the 
junctures at each end of the line to see which of the assignments were 
consistent with one of the 18 possible junctures. This simple test 
turned out to be extremely powerful. 
Sproat's weight table is very analogous with Waltz' list of vertex 
constraints; both define an inventory of global contextual constraints on 
a set of local labels (H and L syllables in this application, and +. -, 
>, < in Waltz application). Waltz' constraint propagation paradigm 
depends on a highly constrained inventory of junctures. Recall that 
only 18 of 208 possible junctures turned out to be grammatical. 
Similarly, in this application there are very strong grammatical 
constraints. According to Spmat's table, there are only 51 distinct 
output stress a.udgnmeats, a very small number considering that there 
are 1020 distinct inputs. 
Pe~ible Stress Assignments 
I 103 3103 020100 0202013 
3 310 02010 020103 2002010 
0l 313 02013 200100 2002013 
31 010O 20010 200103 2020100 
I0 0103 20013 202010 2020103 
13 2001 20100 202013 3202010 
010 2010 20103 320100 3202013 
013 2013 32010 320103 02020100 
100 3100 32013 0202010 02020103 
20020100 
20020103 
20202010 
20202013 
32020100 
32020103 
The strength of these constraints will help make up for the fact that 
the mapping from orthography to weight is usually underdetermined, 
In terms of information theory, about half of the bits in the weight 
representation arc redundant since log 51 is about half of log 1020. 
This means that I only have to determine the weight for about half of 
the syllables in a word in order to assign stress. 
The redundancy of the weight representation can also been seen 
directly from Sproat's weight table as shown below For a one syllable 
noun, the weight is irrelevant. For a two syllable noun, the weight of 
the penultimate is irrelevant. For a three syllable noun, the weight of 
248 
the antipenultimate syllable is irrelevant if the penultimate is light. 
For a four syllable noun, the weight of the antipenultimate is irrelevant 
if the penultimate is light and the weight of the initial two syllables are 
irrelevant if the penultimate is heavy. These redundancies follow, of 
course, from general phonological prin~ples of stresa assignment. 
Weigi~ by Stress (fee short Noum) 
Stress Weight 
! L H 
lO LL HL 
13 LH HH 
010 LHL 
310 HHL 
013 LHH 
313 HHH 
100 HLL LLL 
103 LLH HLH 
0100 LHLL LLLL 
3100 HHLL HLLL 
0103 LLLH LHLH 
3103 HLLH HHLH 
2010 LLHL HHHL 
2013 LHHH HLHH 
LHHL HLHL 
LLHH HHHH 
7. Ore~ - w~ 
For practical purposes, Sproat's table offers a complete solution to the 
weight -- stress subtask. All that remains to be solved is: orthography 
weight. Unfortunately, this problem is much more dif~cult and 
much less well understood. 1'11 start by discussing some easy _~_,-e~, 
and then introduce the pseudo-weight heuristic which helps in some o\[ 
the more di~icuit cas~. Fortunately, l don't need a complete solution 
to orthography ~ weight since weight ~ stress is so well constrained. 
In easy cases, it is pmsible m determine the weight directly for the 
orthography. For example, the weight of torment must be "HH" 
because both syllables arc cloud (even after stripping off the final 
consonant). Thus, the stress of torment is either "31" or "13" stress 
depending on whether is has 0 or I extrametricai final syllables:" 
(strop-from-weights "HH" 0) -- ('31") ; verb 
(stress-from-weights "HH" l) -- ('13") ; noun 
However, meet cases are not this easy. Consider a word like record 
where the first syllable might be light if the first vowel is reduced or it 
might be heavy if the vowel is underlyingly long or if the first syllable 
includes the /k/. It seems like it is imix~sstble to say anything in a 
case like this. The weight, it appears is either "LH" or "HH'. Even 
with this ambiguity, there are only three distinct stress assignments: 
01, 31, and 13. 
AaueUy, ~ practk~. ~ ~l~t det~mm~on is ~mp~aud by t0,,, Smm~5~ 
-crazy ted -ew m, lht be mmx~. New, for example, ths| the tdj~:tiw ~ den 
~ m'~/ike the '.~ mrm~w bin:sum Uul sdjm:trmd e~ .~w ie mumuneuncaL 
(stress-from-weights "LH" 0) -- ('01 ") 
(strm.(rom.weights "HH" 0) -- ('31") 
(sirra-from-weights "LH" I) -- ('13") 
(streas-from-weights "HH" l) -- ('13") 
8. Pmdee-Wekdn 
In fact. it is possible now to use the stress to further constrain the 
weight. Note that if the first syllable of record is light it must also be 
unstressed and if it is heavy it also must be stressed. Thus, the third 
line above is inconsistent. 
I implement this additional constraint by assigning record a pseudo- 
weight of "'-H', where the "-." sign indicates that the weight a~sigment 
is constrained to be the same as the stress assigment (either heavy & 
stressed or not heavy & not stressed), \[ can now determine the 
possible stress assignments of the p~eudo-weight ".-H" by filling in the 
""" constraint with all possible bindings (H or L) and testing the 
results to make sure the constraint is met. 
(strew-from-weights "LH" 0) -- ('I)1 ") 
(stress-from-weights "HH" 0) -- ('31 ") 
(stress-from-weights "LH" I) -- ('13") ; No Good 
(stress-from-weights "HH" l) -- ('13") 
Of the four logical inputs, the -- constraint excludes the third case 
which would assign the first syllable a stress but not a heavy weight. 
Thus, there are only three possible input/output relations meeting all 
of the constraints:" 
Wei~ F.xtramen~ad Syllables Smss 
LH 0 (verb) 01 
HH 0 (verb) 31 
HH I (noun) 13 
All three of these possibilities are grammatical. 
The following pseudo-weights are defined: 
Title Constraints Label 
H 
L 
m 
S 
R 
N 
? 
Heavy 
Light 
Unknown 
Superheavy 
Superlight 
Sonorant 
Truly Unknown 
weight -, H; stress is unknown 
weight -- L; stress is unknown 
(weight - H) ~ (stress - O) 
weight - H; stress ~ 0 
weight - L: stress - 0 
(weight - H) =~ (stress - 0) 
weight is unknown: stress is unknown 
The eoun should ~mbebly have the mm tO rtt~. tMm d~ nress \[3. t u~ 
that te exmtmaCricef syllabk Ms 3 ~eus if it is buy% and 0 Irns if it is UZ,~t. l"~e ~es8 of tM estrsme~L-sJ 8ylhd~hr 
is ~ diR'lcz~t ~ is.edict, as dilc~Jsetd ~ou\]. 
249 
\[ have already given examples of the labels H, L and -. S and R are 
used in certain morphological analyses (see below), N is used for 
examples where Hayes would invoke his rule of Sonorant Destr-~ing 
(see below), and ? is not used except for demonstrating the program. 
The procedure that assigns pseudo-weight to orthography is roughly as 
outlined below, ignoring morphology, etymological and more special 
cases than \[ wish to admit. 
1. Tokenize the orthography so that digraphs such as th. gh. wh, ae. 
ai, ei, etc., are single units. 
2. Parse the string of tokens into syllables (assigning =onsonants to 
the right when the location of the syllable boundary is 
ambiguous). 
3. Strip off the final consonant. 
4. For each syllable 
a. Silent e, Vocalic y and Syllabic Sonorants (e.g., .le. -er. 
-re) are assigned no weight. 
b. Digraphs that are usually realized as long vowels (e.g.. oi) 
are marked H. 
c. Syllables ending with sonorant consonants are marked N; 
other closed syllables are marked H. 
d. Open syllables are marked -. 
In practice. I have observed that there are remarkably few stress 
assignments meeting all of the constraints. After analyzing over 
20.000 words, there were no more than 4 possible stress assigments for 
any particular combinatton of pseudo-weight and number of 
extrametrical number of syllables. Most observed combinations had a 
unique stre~ assignment, and the average (by observed combination 
with no frequency normalization) has 1.5 solutions. In short, the 
constraints are extremely powerful; words like record with multiple 
stress patterns are the exception rather than the rule. 
9. Order~ Muitipte Selmime 
Generally, when there are multiple stress assignments, one of the 
possible stress assigments is much more plausible than the others. For 
instance, nouns with the pseudo-weight of "H--L* (e.g., difference) 
have a strong tendency toward antipenultimate stress, even though they 
could have either 100 or 310 stress depending on the weight of the 
penultimate. The program takes advantage of this fact by returning a 
sorted list of solutions, all of which meet the constraints, but the 
solutions toward the front of the list are deemed more plausible than 
the solutions toward the rear of the list. 
(stress-from-weights "l-I--L" I) -- ('100" "3 I0") 
Sorting the solution space in this way could be thought of as a kind of 
default reasoning mechanism. That is, the ordering criterion, in effect, 
assigns the penultimate syllable a default weight of L. unless there is 
positive evidence to the contrary. Of course, this sorting technique is 
not as general as an arbitrary default reasoner, but it seems to be 
general enough for the application. This limited defaulting mechanism 
is extremely efficient when there are only a few solutions meeting the 
constraints. 
This default mechanism is also used to stress the following nouns 
Hottentot Jackendoff balderdash 
ampersand Hackensack Arkansas 
Algernon mackintosh davenport 
merchandise cavalcade palindrome 
nightingale Appelbaum Aberdeen 
misanthrope 
where the penultimate syllable ends with a sonorant consonant (n. r, t). 
According to what has been said so far, these sonorant syllables are 
closed and so the penultimate syllable should be heavy and should 
therefore be stressed. Of course, these nouns all have antipenultimate 
stress, so the rules need to be modified. Hayes suggested a Sonorant 
Dnstressing rule which produced the desired results by erasing the foot 
structure (destressing) over the penultimate syllable so that later rules 
will reanalyze the syllable as unstressed. I propose instead to assign 
these sonorant syllables the pseudo-weight of N which is essentially 
identical to -.* In this way. all of these words will have the pseudo- 
weight of HNH which is most likely stressed as 103 (the correct 
answer) even though 313 also meets the constraints, but fair worse on 
the ordering criteron. 
(stress-from-weights "HNH" I) -- ('I03" "313") 
Contrast the examples above with Adirondack where the stress does 
not back ap past the sonorant syllable. The ordering criterion is 
adjusted to produce the desired results in this case, by assuming that 
two binary feet (i.e., 2010 stress) are more plausible than one tertiary 
foot (i.e., 0100 stress). 
(weights-from-orthography "Adirondack') -- "L-NH" 
(stress-from-weights "L-NH') -- ('2013" "0103") 
It ought to be possible to adjust the ordering criterion in this way to 
produce (essentially) the same results as Hayes" rules. 
tO. M~ 
Thus far, the di~-usion has assumed monomorphemic input. 
Morphological affixes add yet another rich set of constraints. Recall 
the examples mentioned in the abstract, degrhde/dlrgrudhtion and 
tklegruphkei~grophy, which were used to illustrate that stress 
alternations are conditioned by morphology. This section will discuss 
how this is handled in the program. The task is divided into two 
questions: (I) how to parse the word into morphemes, and (2) how to 
integrate the morphological parse into the rest of stress assignment 
procedure discussed above. 
~" N s-d - used to I~ idlm"aL I sm -,ill am mm du~ differeeczs us just~'=d. At 
in,/tram. IU differt~s m~l vm7 ml~ t- aad ¢~rtamly om ~q)rth pin S into h~e. 
250 
The morphological parser uses a grammar roughly of the form: 
word -- level3 (regular-inflection)* 
level3 -- (level3-prefix) * level2 (level3-suffix)* 
level2 -- (levei2-prefix)* levell (level2-suffix)* 
levell ~ (levell-profix)* (syl)* (leveli-suffix)* 
where latinate affixes such as in+. it+, ac+, +ity, +ion. +ire. -al 
are found at level l, Greek and Germanic al~tes such as hereto#, 
un#. under#. #hess. #/y are found at level 2, and compounding is 
found at level 3. The term level refers to Mohanan's theory of Level 
Ordered Morphology and Phonology \[Mohanan\] which builds upon a 
number of well-known differences between + boundary affixes (level I) 
and # boundary affixes (level 2). 
• Distributional Evidence: It is common to find a level \[ affix inside 
the scope of a level 2 affix (e.g., nn#in +terned and form +al#ly), 
but not the other way around (e.g., *in+un#terned and 
• form#1y +al). 
• Wordness: Level 2 affixes attach to words, whereas level I affixes 
may attach to fragments. Thus, for example, in+ and +ai can 
attach to fragments as in intern and criminal in ways that level 2 
cannot *un#tern and *crimin#ness. 
• Stress Alternations: Stress alternations are found at level I p~rent 
parent +hi but not at level 2 as demonstrated by parent#hood. 
Level 2 suffixes are called stress neutral because they do not move 
stress. 
• Level I Phonological Rules: Quite a number of phonological rules 
apply at level I but not at level 2. For instance, the so-called trio 
syllabic will lax a vowel before a level I suffix (e.g.. divine -- 
divin+ity) but not before a level 2 suffix (e.g., dcvine#ly and 
devine#hess). Similarly, the role that maps /t/ into /sd in 
president ~ pre~dency also fails to apply before a level 2 affix: 
president#hood (not *presidence#hood). 
Given evidence such as this, there can be little doubt on the necessity 
of the level ordering distinction. Level 2 affixes are fairly easy to 
implement; the parser simply strips off the stress neutral affixes, 
assigns stress to the parts and then pastes the results back together. 
For instance, paremhood is parsed into parent and #hood. The pieces 
are assigned 10 and 3 stress respectively, producing 103 stress when 
the pieces are recombined. In general, the parsing of level 2 affixes is 
not very. difficult, though there are some cases where it is very difficult 
to distinguish between a level I and !evel 2 affix. For example, -able is 
level 2 in changeable (because of silent • which is not found before 
level I suffixes), but level I in cbmparable (bocause of the strees shift 
from compare which is not found before level 2 suffixes). For dealing 
with a limited number of affixes like .able and -merit, there are a 
number of special purpose diagnnstic procedures which decide the 
appropriate level. 
Level I suffixes have to be strer,,sed differently. In the lexicon, each 
level I suffix is marked with a weight. Thus, for example, the su~ 
+~'ty is marked RR. These weights are assigned to the last two 
syllables, regularless of what would normally be computed. Thus, the 
word civii+ity is assigned the pseudo-weight ---RR which is then 
assigned the correct stress by the usual methods: 
(stress-from-weights "'--RR" 1) -- ('0100" "3100") 
The fact that +ity is marked for weight in this way makes it relatively 
easy for the program to determine the location of the primary stress. 
Shown below are some sample results of the program's ability to assign 
primary stress.* 
% Correct Number of Level 1 
Primary Stress Words Tested Suffix 
0.98 726 +ity 
0.98 1652 +ion 
0.97 345 +ium 
0.97 136 +ular 
0.97 339 +icai 
0.97 236 +cons 
0.97 33 +ization 
0.98 160 +aceeus 
0.97 215 +ions 
0.96 151 +osis 
0.96 26 i 7 +ic 
0.96 364 +ial 
0.96 169 +meter 
0.95 6 i 7 +inn 
0.95 122 +ify 
0.94 17 +bly 
0.94 17 +logist 
0.94 313 +ish 
0.93 56 +istic 
0.92 2626 +on 
0.92 24 +ionary 
0.90 19 +icize 
0.88 52 +ency 
0.82 1818 +al 
0.77 128 +atory 
0.77 529 +able 
These selected results are biased slightly in favor of the program. 
Over all, the program correctly assigns primary stress to 82% of the 
words in the dictionary, and 85% for words ending with a level I affix. 
Prefixes are more difficult than suffixes. Examples such as 
super +fluou~ (levell 1), s;,per#conducwr (level 2), and 
sr, per##market (level 3) illustrate just how difficult it is to assign the 
prefix to the correct level. Even with the correct parse, it not a simple 
matter to assign stress. In general, level 2 pretixes are stressed like 
compounds, assigning primary stress to the left morpheme (e.g., 
¢,ndercarriage) for nouns and to the right for verbs (e.g., undergb) and 
adjectives (e.g., ;,ltracons~rvative), though there seem to be two classes 
of excentions. First. in technical terms, under certain conditions 
• Stria M ~ as izatma, acl~lur, lo~rt are really seqm:aces o( se,,erat at~xes. In order 
tO avoid some difficult psrun| ~ I da:ided not to allow more than one level I sm~a par ward. This limitinuGa 
requires that \[ enter ~u~ of Icv©l I sut~x~ 
into the Im 
251 
\[Hayes. pp. 307-309\]. primary stress can back up onto the prefix: (e.g., 
telegraphy). Secondly, certain level 1 suffixes such as +ity seem to 
induce a remarkable stress shift (e.g., sfiper#conductor and 
si~per#conductDity), in violation of level ordering as far as I can see. 
For level 1 suffutes, the program assumes the prefixes are marked light 
and that they are extrametricai in verbs, but not in nouns. Prefix 
extrametrieality accounts for the well-known alternation p~rmit (noun) 
versus permlt (verb). Both have L- weight (recall the prefix is L)o 
but the noun has initial struts since the final syllable is extrametrical 
~hereas the verb has final stress since the initial syllable is 
extrametrical. Extrametricality is required here, __hec:_use otherwise 
both the noun and verb would receive initial stress. 
tt. Ety=aetn 
The stress rules outlined above work very well for the bulk of the 
language, but they do have difficulties with certain loan words. For 
instance, consider the Italian word tort6nL By the reasoning outlined 
above, tortbni ought to stress like c;,lcuii since both words have the 
same part of speech and the same syllable weights, but obviously, it 
doesn't. In tact. almost all Italian loan words have penultimate stress, 
as illustrated by the Italian surnames: Aldrigh~ttL Angel~tti. Beli&ti. 
/ann~cci. Ita\[ihno. Lombardlno. Marci~no. Marcbni. Morillo. Oliv~ttL 
It is clear from examples such as these that the stress of Italian loans 
is not dependent upon the weight of the penultimate syllable, unlike 
the stress of native English words. Japanese loan words are perhaps 
even more striking in this respect. They too have a very strong 
tendency toward penultimate stress when (mis)pronounced by English 
speakers: Asah&a. Enom o. Fujimhki. Fujim&o. Fujim;,ru. 
Funasl, ka, Toybta. Um~da. One might expect that a loan word would 
be stressed using either the rules of the the language that it was 
borrowed from or the rules of the language that it was borrowed into. 
But neither the rules of Japanese nor the rules of English can account 
for the penultimate stress in Japanese loans. 
I believe that speakers of English adopt what i like m call a pseudo- 
foreign accent. That is. when speakers want to communciate that a 
word is non-native, they modify certain parameters of the English 
stress rules in simple ways that produce bizarre "foreign sounding" 
outputs. Thus, if an English speaker wants to indicate that a word is 
Japanese, he might adopt a pseudo-Japanese accent that marks all 
syllables heavy regnardless of their shape. Thus, Fujimfira, on this 
account, would be assigned penultimate stress because it is noun and 
the penultimate syllable is heavy. Of course there are numerous 
alternative pseudo-Japanese accents that also produce the observed 
penultimate stress. The current version of the program assumes that 
Japanese loans have light syllables and no extrametricality. At the 
present time, I have no arguments for deciding between these two 
alternative pseudo-Japanese accents. 
The pseudo-accent approach presupposes that there is a method for 
distinguishing native from non-native words, and for identifying the 
etymological distinctions required for selecting the appropriate 
pseudo-accent. Ideally, this decision would make use of a number of 
phonotactic and morphological cues, such as the fact that Japanese has 
extremely restricted inventory of syllables and that Germanic makes 
heavy use of morphemes such as .berg, wein. and .stein. 
Unfortunately, because I haven't had the time to develop the right 
model, the relavant etymological distinctions are currently decided by a 
statistical tri-gram model. Using a number of training sets (gathered 
from the telephone book, computer readable dictionaries, 
bibliographies, and so forth), one for each etymological distinction. I 
estimated a probability P(xyz~e) that each three letter sequence xyz is 
associated with etymology e. Then. when the program sees a new 
word w, a straightforward Baysian argument is applied in order to 
estimate for each etymology a probability P(eb*) based on the three 
letter sequences in w. 
I have only just begun to collect training sets, but already the results 
appear promising. Probability estimates are shown in the figure below 
for some common names whose etymology most readers probably 
know. The current set of etymologies are: Old French (OF). Old 
English (OE), International Scientific Vocabulary (ISV), Middle 
g~e~o~ 
Acesta 
Aivarado 
Alvarez 
Andersen 
Beauchamp 
Bornstein 
Calhoun 
Callahan 
Camacha 
Camero 
Campbell 
Castello 
Castillo 
Castro 
Cavanaugh 
Chamberlain 
Chambers 
Champion 
Chandler 
Chavez 
Christensen 
Christian 
Christian~-n 
Churchill 
Faust 
Feticiano 
Fernandez 
Ferrnra 
Ferrell 
Raherty 
Flanagan 
Fuchs 
Gallagher 
Gallo 
Galloway 
Garcia 
from Orthography 
0.96 SRom 
0,92 SRom, 0.08 
1,00 SRom 
0.95 Swed 
0.47 MF 0.45 
1.00 Ger 
1.00 NBrit 
1.00 N Brit 
0.89 SRom 
0.77 SRom 0.18 
1.00 N Brit 
1.00 SRom 
1.00 SRom 
0.73 SRom 0,17 
1.00 NBrit 
0.86 OF O. 13 
0.37 Core 0.3 l 
0.73 OF 0.20 
0.41 OF 0.25 
1.00 SRom 
0.74 Swed 0. 1.5 
0.63 Core 0.25 
0.gl Swed 0.I0 
0.62 OE 0.17 
0.40 Gcr 0.38 
1.00 SRom 
1.00 SRom 
0.79 SRom 0.17 
0.73 SRom 0.08 
1.00 NBrit 
0.97 NBrit 
1.00 Get 
0.67 NBrit 0.33 
1.00 SRom 
I 0.65 OF 0.19 
0.95 SRom 
OF 
L 
MF 
MF 
MF 
ME 
Get 
Swed 
Core 
Core 
OF 
L 
ME 
SRom 
ME 
252 
French (MF). Middle English (ME). Latin (L). Gaelic (NBrit). 
French (Fr). Core (Core). Swedish (Swed). Ru~lan (Rus). Japanese 
(Jap). Germanic (Get), and Southern Romance (SRom). Only the 
top two candidates are shown and only if the probability estimate is 
0.05 or better. 
As is to be expected, the model is relatively good at fitting the training 
data. For example, the following names selected from the training 
data where run through the model and assigned the label Jap with 
probability 1.00: Fujimaki, Fujimoto. Fujimura. Fujino. Fujioka. 
Fujisaki. Fujita, Fujiwara. Fukada. Fukm'. Fukanaga. Fukano. 
Fukase. Fukuchi. Fukuda. Fukuhara. Fukui. Fukuoka. FukusMma. 
Fukutake. Funokubo, Funosaka. Of 1238 names on the Japanese 
training list, only 48 are incorrectly identified by the model: Abe. 
Amemiya. Ando. Aya. Baba. Banno. Chino. Denda. Doke. Oamo. 
Hose. Huke. id¢. lse. Kume. ICuze. Mano. Maruko. Marumo. 
Mosuko. Mine. Musha. Mutai. Nose. Onoe. Ooe, Osa. Ose. Rai. Sano. 
gone. Tabe. Tako. Tarucha. Uo. Utena. Wada and Yawata. As these 
exceptions demonstrate, the model has relatively more difficulty with 
short names, for the obvious reason that short names have fewer tri- 
grams to base the decision on. Perhaps short names should be dealt 
with in some other way (e.g.. an exception dictionary). 
I expect the model to improve as the training sets are enlarged. It is 
not out of the question that it might be possible to train the model on a 
very large number of names, so that there is a relatively small 
probability that the program will be asked to estimate the etymology of 
a name that was not in one of the training sets. If. for example, the 
training sets included the I00OO must frequent names, then mint of the 
names the program would be asked about would probably be in one the 
training sets (assuming that the results reported above for the 
telephone directories also apply here). 
Before concluding. I would like to point out that etymology is not just 
used for stress assignment. Note. for instance, that orthographic ch 
and gh are hard in Italian loans Macchi and spaghetti, in constrast to 
the general pattern where ch is /ch/ and /ghJ is silent. In general. 
velar softening seems to be cooditionalized by etymology. Thus, for 
er, ample" /g/ is usually soft before /I/ (as in ginger) but not in girl 
and Gibson and many other Germanic words. Similarly. other 
phonological rules (especially vowel shift) seem to be conditionalized 
by etymology. \[ hope to include these topics in a longer version of this 
paper to be written this summer. 
12. Cmc~l~t Remarks 
Stress assignment was formulated in terms of Waltz' constraint 
propagation paradigm, where syllable weight played the role of Waltz' 
• labels and Sproat's weight table played the role of Waltz' vertex 
constraints. It was argued that this formalism provided a clean 
computational framework for dealing with the following four linguistic 
issues: 
• Syllable Weight:. oh@ /deviffop 
* Part of Speech:. t~rment (n) / torment (v) 
• Me~. degrhde /dbgradhtion 
• Etymo/o~: c/'lculi I tortbni 
Currently. the program correctly assigns primary streets to 82% of the 
words in the diotionary. 
Refm 
Chomsky. N.. and Halle, M., The Sound Pattern of English. Harper 
and Row, 1968. 
Hayes. B. P., A Metrical Theory of Stress Rules, unpublished Ph.D. 
thesis, MIT. Cambridge. MA., 1980. 
Liberman, L., and Prince, A.. On Stress and Linguistic Rhythm, 
Linguistic inquiry 8, pp. 249-336, 1977. 
Mohanan. K., lacxical Phonology, MIT Doctoral Dissertation. 
available for the Indiana University Linguistics Club. 1982. 
Waltz. D., Understanding Line Drawings of Scences with Shadows. in 
P. Winston (ed.) The Psychology of Computer Vision, McGraw-Hill. 
NY, 1975. 
253 
