Behavior ResearchMethods. Instruments. & Computers
t996, 28 @, s04-5 t 5
Statistical analysis of the bidirectional
inconsistencyof spelling and sound in French
JOHANNESC. ZIEGLER
Marseille, France
Centerfor Researchin Cognitiue Neu,roscience,
ARTHUR M. JACOBS
e, Mars eille, Franc e
Centerfor Research in Cogniti,ae N eurosc'i,ertc
and Philipp s-Uniuersity, Marburg, Germany
and
GREGORYO. STONE
Arùzona State Uniuersity, Tempe,Ari,zona
Recentstudiessuggestthat perforrnanceattendant on visual word perception is affected not only
by the "traditional" feedforwardinconsistency(spelling-+ phonology)but also by its feedbackinconsistency(phonology+ spelling).The presentstudy presentsa statisticalanalysisof the bidirectional
inconsistencyfor all French monosyllabicwords. We show that French is relatively consistentfrom
spellingto phonologybut highly inconsistentfrom phonologyto spelling.AppendixesB and C list prior
and conditional probabilities for all inconsistentmappingsand thus provide a valuabletool for controlling, selecting,and constructing stimulus materials for psycholinguisticand neuropsychological
research.Such large-scalestatistical analysesabout a language'sstructure are crucial for developing metrics of inconsistency,generatinghypothesesfor cross-linguisticresearch,and building computational models of reading.
When studying Frenchas a secondlanguage,students
often complain that Frenchis unpredictableand ambiguous (Content,l99l ). In a similar vein, Frenchis often said
to be highly inconsistent.Inconsistencytypically refersto
mapping.For inthe ambiguity in the spelling-to-sound
stance,in English,the spellingbody -int rs inconsistent
becauseit can be pronounced latntl as in pint and lntl
as in mint In contrastto French'sreputationas an inconsistentlanguage,recentestimatessuggestthat Frenchorthographyis more consistentthan Englishorthographyas
mappingis concerned(Confar as the spelling-to-sound
tent & Peereman,1992).Gak ( 1976)claimed that95oÂof
wordscould be correctlydecipheredon the basisof rules
convertingspellingto sound.The most preciseestimate,
taken from studieson automatic text-to-speechtranslation, lists no more than 204 exceptionwords (Catach,
1984).Thus,Frenchseemsto be more consistentthan in"inconsistencymystuition might suggest.To resolvethis
The researchreported in this article was supportedby a German
A c a d e m i cE x c h a n g eS e r v i c eG r a n t ( D A A D - D o k t o r a n d e n s t i p e n d i u m
a u s M i t t e l n d e s z w e i t e n H o c h s c h u l s o n d e r p r o g r a m m sa)w a r d e dt o
J . C . Z i e g l e r .W e t h a n k M . C o l t h e a r ta n d D . M a s s a r of o r s t i m u l a t i n g
d i s c u s s i o n so n t h i s a n d r e l a t e dt o p i c s a n d R . S . B e r n d t , S . R a n s d e l l ,
and an anonymousreviewer for valuable feedbackon an earlier version of this article. We are grateful to A. Content, M. Montant, and
A . R e y f o r t h e i r h e l p w i t h t h e s u b t l e t i e so f F r e n c ho r t h o g r a p h ya n d
phonology.Correspondenceshould be addressedto J. C. Ziegler,Cens ognitivesC
, e n t r eN a t i o n a ld e l a
t r e d e R e c h e r c h ee n N e u r o s c i e n c eC
R e c h e r c h eS c i e n t i f i q u e 3
, l , C h e m i nJ o s e p hA i g u i e r , 1 3 4 0 2M a r s e i l l e
C e d e x2 0 , F r a n c e( e - m a i l : z i e g l e r @ ) l n f . c n r s - m r s . f r ) .
tery" of French,detailedstatisticsabout the bidirectional
mappingof spellingand soundare needed.
For English,the mappingof spellingto soundhasbeen
describethe spellingstudiedextensively.Psycholinguists
to-soundstructurein terms of rules that relategraphemes
to phonemes(Venezky,1970;Wrjk, 1966).Neuropsychologists use inconsistencyin the spelling-to-soundmappings as one of severaltools for investigatingdifferent
types of dyslexia(Patterson,Marshall,& M. Coltheart,
& Morton, 1985;Plaut& Shallice,1993).
1985;Patterson
Educatorsand psychologistsanalyzethe way in which children might learn to translatespellingto sound(Bosman
& Van Orden,in press;V Coltheart& Leahy,1992;Treiman, Mul l enni x, B i j el j ac-B abi c,& R i chmond- Welt y,
1995; Waters,Seidenberg,& Bruck, 1984; Wimmer &
Goswami, 1994).Cognitive psychologistsproposemodels and theoriesto accountfor the variousfacetsof inconsistencyobservedin naming and lexical decisiontasks
(M. Coltheart,1978; Forster& Chambers,1973; Frederiksen& Kroll, 1976;Glushko, 1979:'Taraban& McWaters&
Clelland,1987;Van Orden& Goldinger,1994',
Seidenberg,1985).Finally,proponentsof computational
modelsare challengedto implementprocessesin simulation models that capturehuman performancefor inconsistentwords(Brown, 1987;M. Coltheart,Curtis,Atk i n s , & H a l l e r , 1 9 9 3 ; M . C o l t h e a r t& R a s t l e , 1 9 9 4 :
& McClelland,1989;VanOrden,
Norris, 1994:Seidenberg
Bosman,Goldinger,& Farrar,in press).
Not surprisingly,detailedstatisticaldescriptionsof the
spelling-to-soundrelation in English are availableand
504
Copyright 1996PsychonomicSociety,[nc.
505
ZIEGLER.JACOBS.AND STONE
providevaluableresearchtools for planningexperiments
and constructingstimulus materials(Berndt, D'Autrechy,
& Reggia,1994;Berndt,Reggia,& Mitchum, 1987;P.R.
Hanna,J. S. Hanna, Hodges,& Rudorf,1966). Detailed
analysesof a language'sstructure seem crucial for developing and testingmodelsof readingin which the processingof one linguistic item is influencedby the entire
set of items the model knows, as in current connectionist models of reading (Grainger & Jacobs,1996;Jacobs
& Grainger,1992,.1994;
McClelland& Rumelhart,1981;
Stone& Van Orden, 1994;Ziegler,Rey,& Jacobs,1995;
seeTreiman et a1.,1995,for a similar argument).
To determinewhethera monosyllabicword is inconsistent, it is typically broken down into its onsetand spelling body.The onsetis the initial sequenceof consonants,
and the spelling body (or rime) is everything following
it (see,e.g., Patterson& Morton, 1985;Treimanet al.,
1995).For example,pint can be divided into the onsetpand the spellingbody -int.Words are traditionally classified as inconsistentif their spellingbody maps into more
thanonepronunciation(e.g.,-int rnpintvs. mint).They are
traditionally called consistentif their spelling body has
only one possiblepronunciation(e.g.,-uck in duck,luck).
In numerousstudies,the inconsistencyof the spellingto-soundmappinghasbeenshownto affect performance
in lexical decision and naming tasks (Andrews, 1982;
V Coltheart & Leahy, 1992; Content, l99I; Content &
Peereman,1992; Glushko, 1979; Jared,McRae, & Seidenberg,1990; Seidenberg,Waters,Barnes,& Tanenhaus, 1984;Taraban& McClelland, 1987;Waters& Seidenberg,1985). In the naming task, it takes longer to
read aloud inconsistentwords, such aspint, than it does
to readconsistentwords suchasduck (-int in pinl may be
pronouncedas in mint; -uck in duck is pronouncedonly
as in duck). Occasionally,skilled readersmake regularization errors. They may incorrectly pronounce pint to
rhyme with mint Those regularizationerrors are characpatients.In the lexicaldecision
teristicof surface-dyslexic
task,participantsproduceslowerresponsesand more errors to inconsistentitems than to consistentitems.These
consistencyeffects are strongerfor low-frequencythan
for high-frequencyinconsistentitems and are often statistically reliable only for low-frequencywords (but see
Jared, 1995, for consistencyeffects for high-frequency
words).To summarize,thesespelling-+ phonologyconsistencyeffects raise the questionof how reading is affectedwhen a spellinghasmore than one pronunciation.
ogy, words are feedforward inconsistent if a word's
spellingbody hasmore than one possiblepronunciation,
such as -int in pint and mint. Words arefeedback inconsistent if a word's phonologic body has more than one
possiblespelling,suchas l-ipl in deep andheap.ln fact.
Stoneet al. found that lexical decisionlatenciesto words
that were traditionally labeledas consistentwere longer
if they were feedbackinconsistentthan if they were feedback consistent.
In a different line of research, Ziegler and Jacobs( I 995;
seealso Ziegler,Van Orden, & Jacobs,in press)recently
demonstratedthat feedback inconsistencyalso affects
performancein simplegraphemictasks.In a lettersearch
task, they presentedpseudohomophones,
l i ke br ane.
Pseudohomophones
and homophonesare,by definition,
feedback inconsistentbecausetheir phonology can be
spelledin more than one way.The authorsfound that letter detection performance was worse (longer reaction
times [RTs] and more errors) for feedback-inconsistent
letterstrings(i.e.,pseudohomophones)
than for feedbackconsistentspellingcontrols.
In previousstudiesof visual word recognition,inconsistencyin terms of soundto spelling(feedbackinconsistency)has been a neglectedsourceof information.
However,a statisticalanalysisof all English monosl'llabic words showedthat feedbackinconsistencyis common. Stoneet al. (in press)calculatedthat 76.60,'o
of the
monosyllabic words taken from Kuëera and Francis
(1967)were feedbackinconsistentand72.49/o
of the uord
occurrences
were feedbackinconsistent.
On the basisof
theseresults,they concludedthat the ignored factor of
feedbackinconsistencycould have been responsiblefor
small and unreliableconsistencyeffectsin prerious studies,becauseinvestigatorsmight havefailed to control for
feedbackinconsistencyin their consistentcontrol items
(againstwhich the processingcostsof inconsistentitems
w ere tested)-that i s, w hetherthei r consi sten tu'or ds
contai nedphonol ogi cbodi esthat coul d be spelledin
more than one way. To summarize.recent studieshave
shown that it mattersin visual word perceptionu'hen a
pronunciationhasmore than one spelling.Furthermore,
feedbackinconsistencyis rathercommon in English.In
the presentstudy,we look at feedforwardand feedback
inconsistencyin French.
The Present Study
The study and statisticalanalysisby Stoneet al. (in
press)provides a key to understandingthe aforementioned"inconsistencymystery"of French.Frenchmight
A New Perspective
Until recently,all discussionof consistencyeffectshas be relatively consistentfrom spelling to phonology,but
concerneda classic "feedforward" spelling -+ phonol- much lessconsistentfrom phonologyto spelling.In the
ogy effect. However, Stone, Vanhoy,and Van Orden (in presentstudy,we investigatethis hypothesis.We provide
perspec- a statisticaldatabasegiving the degreeof inconsistency
press)challengedthis "one-way-inconsistency"
tive. They demonstratedthat visual word perceptionis not in both directions:from spelling to phonology (feedforonly influenced by "traditional" spelling-to-phonology ward) and from phonology to spelling (feedback).Furinconsistencybut also by phonology-to-spellingincon- thermore,we compareinconsistencyin English to insistency(i.e., when a phonologicbody maps into more consistencyin French.In doing so, we hope to stimulate
than one spelling). According to Stoneet al.'s terminol- cross-linguisticresearchby generatinghypothesesabout
BIDIRECTIONALINCONSISTENCY
506
differencesacrossand similarities between languages. Prior and Conditional Probabilities
As in the Berndtet al. ( 1987)analysisof Englishgrapheme-toThe presentAppendixesB and C also provide a useful
prior and conditionalprobabilities
tool for selectinginconsistentstimuli or creatingpseudo- phonemecorrespondences,
were calculatedfor all spellingand phonologicbodies.Prior
words to test subjectsor patients.
METHOD
Corpus
For the present analysis, we used a computerized lexical database for French (BRULEX) developed by Content, Mousty,
and Radeau(1990). BRULEX contains 35,746lexical entries;
created in 1986, it contains the major part of word entries listed
in the dictionary Micro-Roberr (Robert, 1986). Proper names
and affixes are excluded. Verbs occur in their infinitive form only.
The feminine form of nouns and adjectives (e.g., bon*bonne) is
represented separately. For every word entry, different information is available, such as orthographic form; phonologic form;
grammatical class; word frequency; imageability values; and
number of letters, phonemes, syllables, homographs, homophones,
and meanings.
BRULEX's phonologic representations are based on the phonetic
descriptions found inthe Micro-Robert (Robert, 1986). A key to the
phonetic symbols is presented in Appendix A. It lists the standard
phonetic symbols (according to Warnant, 1987), the keyboardc o m p a t i b l e c o r r e s p o n d e n c e su s e d i n t h e p r e s e n t a r t i c l e a n d i n
BRULEX, and example words.
Word frequency counts in BRULEX are taken from the corpus
"Trésor
d e l a L a n g u e F r a n ç a i s e "( I m b s , l 9 7 l ) . T h i s c o r p u s c o n tains 23.5 million words taken from a wide range of printed materials published between 1919 and 1964.
In the present study, the statistical analysis of inconsistency is
r e s t r i c t e dt o m o n o s y l l a b i c w o r d s ( N : 1 , 8 4 3 ) i n o r d e r t o e n s u r e
compatibility with analyses recently provided for English (Stone
e t a l . , i n p r e s s ;T r e i m a n e t a l . , 1 9 9 5 ) .
Word Decomposition
All monosyllabic words were broken down into their initial onset
(consonant cluster) and their spelling body. For example, pi nt was
divided into the onsetp- and the spelling body -int. For all spelling
bodies, the corresponding phonologic bodies were extracted. Similarly, for all phonologic bodies, the corresponding spelling bodies were extracted.
Feedforward Consistency
A spelling body was feedforward consistent if it mapped into one
and only one phonologic body. All words containing this spelling
body were feedforward consistent.A spelling body was considered
feedforward inconsistent if it could be mapped into more than one
phonologic body. For example, the spelling body -int has more than
one phonologic body, latntl as in pint and lnTl as in mint. Therefore, the spelling body -int is feedforward inconsistent and all
words containing the spelling body -int are feedforward inconsistent.
Feedback Consistency
A phonologic body was feedback consistentif it mapped into one
and only one spelling body. All words containingthis phonologic
body were feedback consistent.A phonologic body was considered
feedback inconsistent if it could be mapped into more than one
spelling body. For example, the English phonologic body /-ob/
can only be spelled -obe. Therefore, the phonologic body /-ob/
and all words containing it (e.g.. probe) are feedback consistent.
In contrast, the phonologic body l-ipl can be spelled -eep and
-eap. Therefore, the phonologic body /-ip/ and all words containing it (e.g., deep or heap) are feedback inconsistent.
probabilities were obtainedby dividing the total frequencyof a
particularbody by the total numberof occurrences
of all bodies.
Conditionalprobabilitiesfor a particularbody wereobtainedby diby the total
viding the frequencyof eachof its correspondences
frequencyof this particularbody. Conditionalprobabilitiescan
alsobe relatedto Jaredet al.'s( 1990)notionof a word's"friends"
(wordswith a similarspellingpatternanda similarpronunciation)
and "enemies"(wordswith a similar spellingpatternbut a differA conditionalprobabilitygreaterthan .5 indient pronunciation).
catesthat the summedfrequencyof a word'sfriendsis greaterthan
the summedfrequencyof its enemies.A conditionalprobability
smallerthan .5 indicatesthat the summedfrequencyof a word's
enemiesis greaterthan the summedfrequencyof its friends.Accordingto Jaredet al., the sizeofthe consistencyeffectis related
to the summedfrequencyof a word'sfriendsand enemies.
RESULTS
AND DISCUSSION
Appendix B givesall feedforward-inconsistentspelling
bodies along with their prior and conditional probabiliphonoties. Appendix C gives all feedback-inconsistent
logic bodies with their prior and conditionalprobabilities. The following sectionsgive summary statisticson
bidirectionalconsistencyin termsof the numberof words
and the number of word occurrences.
Words
The most striking result of the presentanalysisis that
79J% of all monosyllabicFrenchwords are feedbackinconsistent(their phonologic body has more than one
spelling),whereasonly 12.4%are feedforwardinconsistent (thei r spel l i ng body has more than one pr onunciation). In English,approximately760Âof all the monosyllabic words are feedback inconsistent and 33o are
feedforwardinconsistent(seeStoneet al., in press).Table 1
summarizesthe crossedanalysisfor feedforwardand
feedbackinconsistency.
If we look at all the words that would traditionally be
cl assi fi edas " consi stent"on the basi sof spelling- t o(87.6% of all the words),77.4oÂ
soundcorrespondences
of them are feedbackinconsistent.Therefore, on average, up to 8 out of l0 items chosenby investigatorsas
Table I
Analysis of Crossed Consistency Conditions Based on the
Number of Words Within Each Condition
Feedback
Consistenl
Inconsistent
o/o
n
Feedforward
Consistent
Inconsistent
364
2l
19.8
l.t
l,250
208
6 7. 8
I 1.3
1,614
229
I
38s
20.9
l .458
79.1
l ,843
8 7. 6
12.4
Note-"Feedforward" refersto the mapping of spellingto sound,
whereas"feedback" refers to the mapping of sound to spelling.
507
ZIEGLER.JACOBS.AND STONE
"consistent"are,in fact, feedbackinconsistent.This might
explain contradictory and unreliable findings concerning consistencyeffectsin English and French.
Given the high degreeof feedbackinconsistency,an
interestingquestionconcernsthe averagenumberof possible spellings(also called "gangs") for each inconsistent phonologicbody.An analysistaking into accountall
feedbackinconsistentwords revealsthat an inconsistent
phonologic body has, on average,3.67 possiblespellings. Someinconsistentphonologicbodies,like /e/, have
up to l3 different spelling bodies. If a major part of all
the words (79.1oÂ)can,on average,be spelledin threeor
more different ways, it is no longer surprisingwhy French
hasintuitively beenconsideredas unpredictableand ambiguous.Having many ways to spell a given sound leads
to a high degreeof homophony,a key featureof French
that is known not only for its capacity to generatemisin spokenlanguage,but also for allowing
understandings
some beautiful puns. On the basis of this bidirectional
consistencyanalysis,it can be concludedthat monosyllabic Frenchwords are highly ambiguousfrom soundto
spellingbut much lessso from spellingto sound.In more
popularterms,Frenchmight be easyto readbut difficult
to spell.Although theseconclusionsarebasedon the analysis of monosyllabicwords only, monosyllabicwords
are more frequentlyusedthan are more complex words.
The meanusagefrequencyof Frenchmonosyllabicwords
is 179 occurrencesper million as opposedto 35 occurrencesper million for all other words in BRULEX.
Table 2
Analysis of Crossed-Consistency Conditions Based on the
Number of Word Occurrences (in Hundreds/Million)
Feedback
Consistent
o/o
n
Inconsistent
n
o
Â
Feedforward
79.7
Consistent
Inconsistent 6.3
2.6
0.2
2 , 3 7 ' 7. 8
560.9
78.6
18.5
2 . 4 5 7. 5
5 6 7. 2
86.0
2.8
2,938.7
97.2
3.024.'
I
8 I .2
I 8.8
Note-"Feedforward" refers to the mapping of spelling to sound.
"feedback" refers to the mapping of sound to spelling.
whereas
of monosyl l abi cw ords i s somew hathi gher tha n t he
usagefrequencyfor all other words in BRULEX.
In previous studies, inconsistencyand irregularity
haveoften beentreatedas a binary variable.However,the
degreeof (in)consistencyalso seemsto be important
(Jaredet al., 1990).Therefore,metricsof inconsistency
mapare needed.In fact, as concernsspelling-to-sound
pi ngs, some researchershave proposedsuch met r ics
(Massaro& Cohen,1994:Rosson,1985:Venezkr'&Massaro, 1987).Our empi ri cal l yderi vedpri or and c onditionalprobabilitieslistedin AppendiresB andC canalso
basedon the
be usedto generatemetricsof inconsrstencv
asthevoccuri n a cor pusof
bi di recti onali nconsi stenci es
all monosyllabicFrenchu'ords.
C ON C LU S IO\
Word Occurrences
When taking word occurrences(i.e., token count) as
the critical measure,we obtain a complementarypicture:
97.2%of all word occurrencesare feedbackinconsistent,
whereasonly 18.8% of all word occurrencesare feedforward inconsistent.Theseresultsare listed in Table2.
The averagefrequency of occurrencefor feedforwardinconsistentwords (265 permillion) is greaterthan that for
feedforward-consistent
words (167 per million). The difwords.
ferenceis more striking for feedback-inconsistent
The average frequency of occurrence for f'eedbackinconsistentwords (220 per million) is much greater
words (25 per million).
than that for feedback-consistent
Note that words inconsistentin both directionshave an
especiallyhigh averagefrequency(289 permillion). This
analysisconfirms linguistic studiessuggestingthat irregularitiesare more likely in common words than in uncommonwords,becausefrequentlyusedwords are more
likely to survive linguistic evolution in irregular form
thanare lessfrequentlyusedwords (Stoneet al., in press).
Moreover,the pronunciationof words is more likely to
deviatefrom its fossilizeddictionaryversionwhen words
are frequently used in spoken language(Ellis, 1993).
Sinceinconsistencyseemsto be more common in highfrequencythan in low-frequencywords,our analysismight
slightly overestimatethe degreeof inconsistencyfor the
entire set of French words becausethe usagefrequency
In conclusion,a monosyllabicFrenchu'ordselectedat
(79.l c o)but
randomi s l i kel y to be feedbacki nconsi stent
(87
feedback
inconsisfeedforwardconsistent .60Â).Thus.
tency seemsto be the rule in French.n'hereasfeedforward
inconsistency
seemsto be the exception.Sincefeedback
inconsistencyhas been shou,nto affect performancein
lexical decision.naming. and perceptualidentification
tasks(Hooper& Paap.in press:Stoneet al., in press;Stone
& Vanhoy,1994:Ziegler& Jacobs,1995;Ziegleret al., in
needto be
press),stimuli in psycholinguistic
experiments
controlledon this variableand systematiccross-linguistic
researchis neededto further quantify the influence of
feedbackinconsistency.The presentwork is a first step
i n that di recti on.provi di ng a tool for control l i ngand
selectingword stimuli, constructingnonword stimuli,
developingquantitativemetrics of inconsistency,and
generatinghypothesesfor cross-linguisticresearchand
modeling.
Availability
In AppendixesB and C, only inconsistentspelling-toarelisted,
correspondences
soundand sound-to-spelling
becausethose are the most important for constructing,
selecting,and verifying stimulus materials.However,
prior probabilitiesfor all consistentspelling and pronunciationbodies are availablefrom J.C.Z.
BIDIRECTIONALINCONSISTENCY
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tal Ps,,-cholog.v:
APPENDIX A
Kev to PhoneticSvmbols
Standard
Symbol
Corresponding
Symbol
I
I
e
e
a
é
CI
e
a
A
o
o
u
o
v
v
g
æ
e
e
ô
ô
æ
j
w
u
E
e
ê
à
ô
û
i
û
Examples
/dée, ami
ému, ôté
perdu, mère
alarm. patte
baton. paTe
obstacle,corps
auditeur,beau
coupable,loup
punir, êlu
cteuser,deux
malheureux,peur
petit, fortement
matin, fin
ample,temps
rondeur,bon
brun,lundi
briller, pied
oui, fouine
Standard
Symbol
q
p
t
k
b
d
(t
D
Corresponding
Symbol
v
p
t
k
b
d
ç
D
f
f
S
S
I
V
z
3
I
R
n1
n
T
Z
j
I
R
m
n
N
Examples
huile, nuire
patte. repas
tête. ôrer,net
carte,écaille,bec
bête.roôe
dire. chaude
gauche.égal
./èu. chel
.roeur.par.re
chanter.peche
vent, rêr'e
zéro, raison
jardin, manger,
/ong, ba/
rond, charlot
madame,aimer
,?ous,banne
agneau,règne
Note-Taken from Warnant( 1987).
(c'ontinuedon nert page)
BIDIRECTIONALINCONSISTENCY
5IO
APPENDIX B
Prior and conditional probabilitiesfor feedforward-inconsistent
(spelling + phonology) correspondences.
Phonologicbodies that are feedbackinconsistent(phonology-+ spelling)are indicatedby an asterisk(x).
Body
Spelling
at
aid
aie
am
amp
and
ang
Prior
Body
Prior
Probability
Spelling
Probability
Body
Conditional
Phonology Probability Example
*
.0025734
lel
.891793
frai
lél
108207
quai
*
.0001324
lel
.911888
laid
*
ledl
.088112
raid
*
.0003743
lel
.676309
baie
*
lél
gaie
323691
.0000205
laml *
.791525
tram
*
làl
.208475
dam
*
.0006992
làl
.999154
camp
*
làpl
.000846
vamp
*
.0107847
làl
grand
.999425
làdl *
.000575
stand
*
.0007985
làl
.996861
sang
*
làgl
.003139
gang
ap
.0001653
arc
.0001835
ars
.0003309
as
.0344907
at
.0004452
az
.0000g7g
ef
.00r24g5
emme
.0030017
ens
.0033744
erf
.0001365
et
.0028028
eule
.0001334
ez
.00314g4
ier
.00177g3
ile
.0001201
ille
.0034104
lal
lapl
/ank/
las.l
/ans/
lal
lanl
lal
lasl
lal
latl
razr
lal
ltfr
lér
ramr
lxml
làl
làst
ltp.l
lxnfI
retr
lxl
lell
tElt
têt
Itzl
tienl
fiél
tilt
taTlt
tilt
tiit
*
*
*
*
+
*
*
*
x
*
*
*
*
+
*
*
*
*
*
*
*
*
*
*
*
*
.835264
164736
.939469
.060531
.634747
.359465
.005788
drap
cap
parc
marc
mars
gars
jars
.ggg5g1
'000403
.919521
.080479
.94g139
.05I 861
.g25624
174376
bas
mas
chat
mat
gaz
raz
chef
cref
femme
gemme
gens
,.n,
nerf
serf
ner
.997390
'002610
.608990
. 3 9 lo o 9
.9l9g9g
.080102
.972759
'027241
.753850
.246150
.9gg34g
.000652
.6g163g
.31g362
.99gg40
.001160
iet
seule
veule
nez
fez
hier
nier
pile
mile
506529
.493471
mille
fille
in
.0017655
ix
.0018308
oc
.0003276
ome
.0000330
onc
.0025600
ong
.0010633
op
.00291g3
ord
.000g727
os
.0032t36
ot
.002g054
oup
Body
Conditional
Phonology Probability Example
lê,1 *
.997496
fin
*
tint
gin
.002504
+
lisl
.745255
dix
*
lil
prix
.254745
lokl *
.932299
choc
*
lot
.06770t
broc
loml *
.57g669
rome
lOml *
.42133t
home
*
lôkl
.956276
donc
*
jonc
tôl
.043724
*
tôt
.991945
long
lôgl *
gong
.00g055
*
lol
.gg442g
rrop
topt *
.005571
stop
lonl
/ond/
lol
/os/
lol
lotl
'002317|
ours
.000s477
ous
'0369600
out
'0154699
^
oût
*
*
*
9g74g7
.002513
.966957
.033043
.9g9935
.010065
fiord
dos
os
mor
dot
lul
lupl
*
*
'999745
.000255
loup
croup
/un/
/uns/
lul
/us/
lul
lutl
*
*
*
*
x
'0009425
lul
lutl
*
*
.93566g
.064332
'71761852
.28238 148
'999858
'000142
j06154
cours
ours
nous
tous
bout
scout
goût
août
uel
.000030|
*
uet
'0001805
lj,ell
l\ull
lyxl
lel
I'jil
lil
*
*
*
*
.293846
.ggg43g
'ol 156l
'92208293
.07791707
'999626
.000374
duel
fuel
muet
guet
nuit
huit
nul
cul
punch
lunch
turf
surf
plus
jus
ur
'0153595
urt
0035050
riir
tiitt
*
.g70561
129439
ul
.0003574
unch
.0000102
urf
.0000015
.901413
.098587
.593857
.406143
.976744
.023256
us
.0205097
*
lyll
*
lyl
*
lôll
lentll
lynfl
tenft
*
lysl
*
lyl
ut
.0004244
lyl
lvtl
*
nord
. 9 9 8 01 6
.001984
.899262
100738
lui
gui
but
chut
5I I
ZIEGLER,JACOBS,AND STONE
APPENDIX
C
Prior and conditionalprobabilitiesfor feedback-inconsistent
(phonology -+.sp9r]ing)correspondences.
Spelling bodies that
are feedforwardinconsisient(spelling -+ phonology)
!
are indicatedby anîsterisk (*).
Body
prior
Body
Conditional
f"
/a/
làl
làll
làdl
làgl
lail
lakl
/àkl
lall
laml
/anl
ta9/
làpl
lanl
.0433965
3
acs
ah
ap
ars
as
at
az
x<
*
*
'r'
*
*
*
*
.22g512 va
.000544 lacs
. 0 0 11 5 3 b a h
.003021 drap
.002602 gars
.754409 bas
.009659 chat
.000100 raz
*
.0632077 am
.000064 dam
*
amp
.010495 camp
an
.039905 clan
anc
.021377 franc
and
16192g quand
x
ang
.011958 sang
ans
.569777 sans
ant
.0431lg gant
aon
.000347 taon
emps
.063549 temps
*
ens
.050691 gens
ent
.028903 vent
.00093l9
anche
.ggg397 blanche
enche
.000603 clenche
.0003039 ande
.9g0633 grande
*
and
.019367 stand
*
.0003383 ang
.007035 gang
angue
.992965 mangue
.0003594 ail
.t4t4g0
rail
aille
.942870 taille
ailles
.015639 ouailles
.0021269 ac
.t5g22l
sac
ack
.000855 snack
aque
.839924 chaque
.000189
ank
.1331l2 tank
anque
.g66ggg banque
.0032124 al
.596567 mal
ale
.059527 cale
âle
.ll1099 pâle
alle
.232805 balle
*
.0053791 am
.002g70 tram
ame
.418079 dame
amme
.050539 gramme
emme *
.528512 femme
.000249
ane
.656221 cane
anne
.343779 panne
aonne
.001062 paonne
*
.0003125 ap
.0g2734 cap
ape
.499259 rape
appe
.418007 trappe
*
.0003657 amp
.001537 vamp
ampe
.798951 crampe
empe
.199512 rempe
.0229564 aî
.g22679 bar
*
arc
.000459 marc
Body
Prior
Body
Conditional
Phonology Probability Spelling Probability
Example
lankl
/as/
làs/
/aU
làtt
lAzl
tEt
lé/
ard
are
arre
ars
arI
.0001732 ark
arc
.004844 ace
AS
ASSE
.0007504 ance
anse
ens
ense
. 0 0 0 6 1 3 2 at
ate
ath
att
atte
.0009897 ante
ente
enthe
.04ll6l
.027829
.003687
.000079
.104105
. 0 5 51 5 3
.944847
.743358
.002723
.253919
.196077
.065709
.722550
.015662
.0010444 ase
az
aze
.0t4t499 eu
eue
eux
oeu
oeud
ueux
. 0 0 5 0 1 6 1 ai
aie
é
ée
ef
ez
Itl
.0769465 ai
aid
aie
ais
ait
aix
egs
CS
têt
ès
et
êt
ets
uet
.0099082 a i m
ain
aint
ein
*
,r
rË
t&
:F
tard
rare
marre
jars
quarr
mark
parc
trace
mas
rasse
chance
danse
cens
dense
.055478 mat
.602868 date
.009812 math
.000647 watr
. 3 3I 1 9 5 p a r t e
.355647 tanre
.633297 vente
. 0 11 0 5 7 m e n t h e
. 9 1 6 1l 3
vase
.075815 gaz
.008072 gaze
.479380 peu
.007065 queue
.500686 deux
.006589 voeu
.005437 noeud
.000844 queux
.052713 quai
.000007 gaie
.302183 thé
.008239 née
.041245 clef
.595613 nez
.028321 frai
.001490 laid
.003186 taie
.223536 mais
.076548 rrait
.007019 paix
.000082 legs
.591681 ses
.061455 très
.000942 jet
.005262 prêt
.000305 mets
. 0 0 0 17 4 g u e t
.019797 faim
.458233 vain
.104999 sainr
.159420 sein
BIDIRECTIONALINCONSISTENCY
512
Appendix C (Continued)
Body
Prior
Phonology Probability
/e//
.0002242
ledl
.0002943
lefI
.0007312
lefl
.0009904
Body
Spelling
eing
eint
en
in
ingt
ym
aiche
èche
êche
eiche
aid
aide
ed
euf
oeuf
uff
ef
effe
inc
ingue
oeil
euil
euille
aye
eille
lègl
.0000208
leil
.0012533
leil
.00027
t5
lejl
.0002282 ège
lxkl
.0 0 0 7 3 0 9 eak
eige
/êks/
lell
lxll
/emt
lenl
lepl
ec
ecque
eik
èque
. 0 0 0 0 1 5 3 inx
ynx
.0 0 3 7 9 5 3 eul
eule
.0027045 e l
èle
êle
elle
aile
.0 1 2 3 5 1 4 ême
emme
ème
.0024738 aine
ègne
eigne
eine
ène
êne
enne
.0 0 0 0 7 0 7 ep
èpe
êpe
eppe
Conditional
Probability Example
.000057 seing
.008385 teint
.010277 ben
.1 6 8 7 8 3 v i n
.069540 vingt
.00051I
rhym
.005604 fraiche
.723050 sèche
.265153 pêche
.006194 seiche
.037636 raid
. 9 1 51 7 8 a i d e
.047186 bled
.715184 veuf
.277537 boeuf
.007280 bluff
.989084 chef
.010916 greffe
.785032 zinc
.214968 dingue
.509286 oeil
.219927 seuil
.270787 feuille
.012177 paye
.987823 veille
.001738 grège
.998262 neige
.005337 steak
.719604 sec
.241270 grecque
.006333 cheik
.027456 chèque
. 8 5 31 3 2 s p h i n x
.146868 lynx
.974843 seul
.025157 seule
. 8 51 8 7 9 q u e l
.038995 zèle
.007163 prêle
.017822 belle
.084139 aile
.983948 même
.000602 flemme
.015450 thème
.296354 haine
.036378 règne
.009302 peigne
.381993 reine
. 18 5 I 7 6 s c è n e
.088312 frêne
.002486 renne
.230589 hep
.057530 cèpe
.481291 guêpe
.230589 steppe
Body
Prior
Phonoiogy Probability
/eR/
.012997
/eR/
. 0 31 6 8 6 6
/es/
.0008104
Body
Spelling
eur
eure
eurre
eurt
oeur
oeurs
air
aire
er
erc
ère
erf
erre
ers
ert
aisse
CSS
ESSE
letl
.0060217
lètl
.0003868
/evt
.0008771
lxzl
. 0 0 0 71 6 9
tit
.057409
tïilt
.0000125
tidt
.0007247
tiBt
.0053262
tiét
.0016382
liel
.0000813
/te/
. 0 1 5 6 961
aite
ept
et
ête
ette
ainte
einte
inte
inthe
aive
êve
ève
aise
eize
èse
ez
èze
i
id
ie
is
it
ix
iz
ui
iante
iente
ide
uide
ieu
ieue
ieux
ied
ier
iais
iet
ien
iens
Conditional
probability Example
.567154 peur
.22ll19
heure
.003915 leurre
.001687 heurt
.198012 soeur
.008114 moeurs
.087831 chair
.471732 taire
. 0 5 31 7 5 f e r
.002037 clerc
.147925 guère
.003762 nerf
.103733 verre
.099832 vers
.010649 vert
.193260 graisse
.004651 stress
.802089 cesse
.006978 traite
.048791 sept
.478739 net
.514283 fête
.012463 dette
.804615 sainte
.161368 teinte
.027521 quinte
.006496 plinthe
.020844 glaive
.789718 rêve
. I 89439 grève
.688342 fraise
.159657 seize
.146145 thèse
.002721 fez
.003136 pèze
.829580 cri
.001239 nid
.072563 vie
.032809 pris
.055665 dit
.0077l5 prix
.000429 riz
.000095 gui
.264550 riante
.470900 fiente
.873261 vide
.126739 guide
.308614 dieu
.009423 lieue
.681963 vieux
.671833 pied
.328167 nier
.958503 niais
.041497 quiet
.999149 rien
.000851 tiens
sl3
ZIEGLER,JACOBS,AND STONE
Appendix C (Continued)
Body
Prior
Phonology Probability
lieil
l\ell
liexl
/ies/
.0000092
Body
Spelling
ieil
ieille
iel
ielle
.0012133 ier
iers
ierre
.0008058 ièce
iesse
.0010721
l\ezl
.0000102
tiil
.0004685
tikt
.0002467
tilt
.0606600
iaise
ièse
if
iffe
ic
ick
ique
il
ile
ille
yle
île
ime
yme
liml
.0003552
/iN/
.0016475
igne
ygne
linl
.0002919
tipt
.0005753
een
ine
in
ip
ipe
ippe
ype
linl
lisl
tiU
lizl
tot
ir
ire
yre
yrrhe
ice
.0022625
ils
is
iss
isse
ix
ys
.0010121 ite
itte
ythe
.0006215 ise
uise
.0140089
. 0 3 3 3 0 8 8 au
aud
aut
aux
eau
o
o
c
op
o
s
*
'F
*
Conditional
Probability Example
.3 5 9 7 1 2 vi ei l
.640288 vieille
.992568 miel
.007432 vielle
.630473 hier
.0341l7 tiers
.335410 pierre
.994953 pièce
.005047 liesse
5 niaise
.32467
.675325 dièse
.923859 vif'
.0 76 1 4 l gri ffe
.541080 chic
.004423 stick
.454497 tique
.963267 fil
.001877 pile
.027041 ville
.003799 style
.004176 île
.999069 prime
.000931 chyme
.983525 vigne
.016475 cygne
.019426 spleen
.966395 mine
.0 1 4 1 7 9 gi n
.001954 slip
. 1 76 6 5 7 p i p e
.042699 grippe
.778691 type
.0 0 3 5 9 2 ti r
.995325 pire
.000923 lyre
.0 0 0 1 61 myrrhe
.0 5 5 6 88 vi ce
.340352 fils
.012669 lis
.0 0 5 4 07 mi ss
.001914 lisse
.5 7 2 6 60 si x
. 0 1l 3 l 0 l y s
.867610 vite
.037499 quitte
.094891 mythe
.942340 prise
.057660 guise
.564445 au
.006579 chaud
.042580 saut
.0 1 6 6 01 taux
.084983 veau
.023936 ho
.000632 croc
.082734 trop
.0 8 8 5 89 dos
Prior
Body
Phonology Probability
Body
Spelling
*
ot
ôt
tôt
tôil
lobl
.05625190 om
omb
omPt
on
*
onc
ond
onds
{'
ong
ont
onts
.0000153 onche
*
unch
.0004079 ob
obe
toft
.0002229
lofI
.0000208
logl
lôgl
lokl
tôkt
tolt
loll
lOml
loml
lOnl
lopl
/oR/
auf
auffe
of
ophe
.0000528 og
ogue
.0007164 ong
ongue
.0004672 oc
ock
ocque
oq
oque
.0023368 onc
onque
.0007849 all
aule
awl
iole
oal
ôl
ôle
.0012997 ol
ole
olle
.0001613 aume
ome
ôme
.0215893 ome
omme
um
.0004726 aune
ône
aulne
.0000453 op
ope
.0091809 aur
aure
or
orc
ord
{'
*
*
*
*
*
*
Conditional
Probability Example
.079172
.009749
flot
tôr
.022547
.000827
.000969
.833719
.001889
.027369
.000979
.017805
.093857
.000035
.623377
.376623
.044335
.955665
nom
plomb
prompt
ton
tronc
rond
fonds
long
front
fonts
tronche
punch
snob
robe
.994958
.005042
sauf
chauffe
prof
strophe
grog
vogue
gong
longue
choc
stock
socque
coq
toque
donc
conque
hall
gaule
crawl
gniole
goal
khôl
tôle
vol
sole
folle
paume
home
môme
tome
pomme
rhum
jaune
cône
aulne
stop
chope
saur
maure
for
porc
nord
.054054
.945946
.090169
.909831
.011352
.988648
.620719
.092910
.006298
.130343
.149731
.994780
.005220
.046374
.038624
.001432
.000042
.001432
.001053
.911044
.525424
.006995
.467581
.731707
.081719
.186514
.000839
.998542
.000619
.493635
.498824
. 0 1l 8 9 l
.340627
.659373
. 0 0 0 1 5I
.001347
.104339
.002971
.090032
BIDIRECTIONALINCONSISTENCY
514
Appendix C (Continued)
Body
Prior
Phonology Probability
lOsl
Body
Spelling
ore
orps
ors
ort
.0005347 auce
AUSSE
/osl
.0006960
oce
OSSE
OS
lotl
t ôt l
lOzl
lul
lùl
lual
l;jafI
luall
luanl
lùanl
tnét
lùêt
.0009402 acht
ot
ote
otte
. 0 0 1 2 8 s 4 ompte
omte
onte
. 0 0 6 2 5 8 8 ause
ose
.07t5949 ou
oue
oug
oul
ouls
oup
ous
out
oût
outs
oux
.0448345 eun
un
.02603l5
oi
oid
oids
oie
oigt
ois
oit
oix
.0001528 o i f
oiffe
. 0 0 0 5 1 8 3 oêle
oelle
oil
oile
.0000731 oine
ouane
ouenne
.0140626 eoir
oir
oire
.001257
|
oué
ouée
ouer
. 0 0 8 9 5 2 1 oin
oing
*
*
{(
rl.
Conditional
probability Example
.006021 store
.1 9 5 9 55 corps
.074199 hors
.524986 tort
.047610 sauce
.952390 hausse
.096271 noce
.759015 grosse
.1 4 4 7 14 os
.0 1 0 7 6 0 yacht
.028518 dot
.766545 vote
.194177 sotte
.567876 prompte
.113246 comte
.3 1 8 8 7 9 honte
.169947 pause
.830053 prose
.248509 sou
.007134 proue
.000265 joug
.000577 soûl
.0 0 0 2 5 1 poul s
.030725 loup
.490211 vous
.2 0 5 1 5 3 tout
.008828 moût
.000046 touts
.0 0 8 3 0 1 toux
.000134 jeun
.999866 brun
.529184 quoi
.021228 froid
.009288 poids
.0 5 2 8 0 1 voi e
.021152 doigt
.257385 trois
.019896 toit
.089066 voix
.910015 soif
.089985 coiffe
.122353 poêle
.040699 moelle
.1 9 6 4 7 9 poi l
.640470 voile
.775215 moine
.1 9 4 0 3 0 douane
.0 3 0 7 5 5 couenne
.001208 seoir
.671293 voir
.327499 boire
.0 7 1 1 3 7 doué
.077844 rouée
. 8 5l 0 l 9 v o u e r
.242168 soin
.018727 poing
Body
Prior
Phonology Probability
lufl
lukl
lull
/um/
lupl
/un/
/us/
lutl
luzl
tyl
Body
Spelling
oins
oint
.0000595 ouf
ouffe
.0000233 ouc
ouk
ouque
.0006079 ool
oule
.0000064 oom
oum
.0010494 oup
oupe
ouppe
.0284401 our
ourd
ourg
ourre
ours
ourt
.0140204 ouce
ous
ousse
.0027787 out
oût
oute
outte
.0002067 ouse
ouze
ues
.0368807 u
û
ue
ul
US
lydl
.0002936
tiêt
.0005641
lyel
.0002478
lj,ell
.0000844
tyfI
.00000s3
ttit
.0204001
lj,inl
.0004968
ut
UX
ud
ude
uée
uer
uin
uint
uel
uelle
uf
uffe
ui
uie
uid
uis
uit
uits
uy
uir
uire
Conditional
probability Example
.360859 m oins
.378246 point
.260966 pouf
.739034 touffe
.838298 bouc
.150355 souk
. 0 11 3 4 8 c o u q u e
.005275 pool
.994725 poule
.824742 groom
.175258 boum
.000536 croup
.996535 soupe
.002930 houppe
.943543 tour
.023173 sourd
.002151 b our g
.000578 courre
. 0 1 7 l10 c o u r s
.013446 court
.004584 pouce
.985022 tous
.010394 ro usse
.000750 scout
.094647 août
.850647 doute
.053957 goutte
.221565 grouse
.775716 douze
.002720 blues
.904506 vu
.020t40 dû
.054379 grue
.008295 cul
.001048 jus
.009827 but
.001807 flux
.634091 sud
.365909 prude
.148743 nuée
.851257 tuer
.989860 j uin
.010140 suint
.335031 duel
.664969 ruelle
.341614 tuf
.658386 truffe
.714685 l u i
.001489 pluie
.000068 muid
.124368 puis
.142033 fruit
.003846 pu it s
.000110 puy
.68195
I
fuir
.318049 br uir e
515
ZIEGLER,JACOBS,AND STONT,
Appendix C (Continued)
Body
Prior
Phonology Probability
lyitl
tykt
lyll
tvpt
Body
Conditional
Spelling Probability Example
. 0 0 1 6716
uit
uite
.0003393 uc
uque
.0000962 ul
ule
ull
ulle
.0001366 upe
uppe
"
*
.2 6 6 3 36 hui t
.7 3 3 6 64 sui te
.747175 truc
.252825 nuque
.3 4 7 7 66 nul
.239519 mule
.034364 pull
.378351 tulle
.985725 jupe
.014275 huppe
Prior
Body
Phonology Probability
lynl
lysl
Body
Conditional
Spelling Probability Example
.0346906 ur
ure
.0196734 uce
US
lytl
.0006226
( M a n u s c r i p tr e c e i v e dO c t o b e r 1 6 , 1 9 9 5 ;
r e v i s i o na c c e p t e df o r p u b l i c a t i o nF e b r u a r yI , I 9 9 6 . )
usse
ut
ute
uth
utte
.510072
.489928
.001086
.987985
.010930
.065212
.382773
.005841
.546174
sur
dure
puce
b us
russe
chut
brute
l ut h
l ut t e