The grapho-phonological system of written French: Statistical 
analysis and empirical validation 
Marielle Lange 
Laboratory of Experimental Psychology, 
Universit6 Libre de BruxeUes 
Av. F.D. Roosevelt, 50 
Bruxelles, Belgium, B 1050 Bruxelles 
mlange@ulb.ac.be 
Alain Content 
Laboratory of Experimental Psychology, 
Universit6 Libre de Bruxelles 
Av. F.D. Roosevelt, 50 
Bruxelles, Belgium, B 1050 Bruxelles 
acontent@ulb.ac.be 
Abstract 
The processes through which readers evoke 
mental representations of phonological forms 
from print constitute a hotly debated and 
controversial issue in current psycholinguistics. In 
this paper we present a computational analysis of 
the grapho-phonological system of written 
French, and an empirical validation of some of the 
obtained descriptive statistics. The results provide 
direct evidence demonstrating that both grapheme 
frequency and grapheme entropy influence 
performance on pseudoword naming. We discuss 
the implications of those findings for current 
models of phonological coding in visual word 
recognition. 
Introduction 
One central characteristic of alphabetic writing 
systems is the existence of a direct mapping 
between letters or letter groups and phonemes. In 
most languages, although to a varying extent, the 
mapping from print to sound can be characterized 
as quasi-systematic (Plaut, McClelland, 
Seidenberg, & Patterson, 1996; Chater & 
Christiansen, 1998). Thus, descriptively, in 
addition to a large body of regularities (e.g. the 
grapheme CH in French regularly maps onto/~/), 
one generally observes isolated deviations (e.g. 
CH in CHAOS maps onto /k/)as well as 
ambiguities. In some cases but not always, these 
difficulties can be alleviated by considering higher 
order regularities such as local orthographic 
environment (e.g., C maps onto /k/ or/s/ as a 
function of the following letter), phonotactic and 
phonological constraints as well as morphological 
properties (Cf. PH in PHASE vs. SHEPHERD). 
One additional difficulty stems from the fact that 
the graphemes, the orthographic counterparts of 
phonemes, can consist either of single letters or of 
letter groups, as the previous examples illustrate. 
Psycholinguistic theories of visual word 
recognition have taken the quasi-systematicity of 
writing into account in two opposite ways. In one 
framework, generally known as dual-route 
theories (e.g. Coltheart, 1978; Coltheart, Curtis, 
Atkins, &Haller, 1993), it is assumed that 
dominant mapping regularities are abstracted to 
derive a tabulation of grapheme-phoneme 
correspondence rules, which may then be looked 
up to derive a pronunciation for any letter string. 
Because the rule table only captures the dominant 
regularities, it needs to be complemented by 
lexical knowledge to handle deviations and 
ambiguities (i.e., CHAOS, SHEPHERD). The 
opposite view, based on the parallel distributed 
processing framework, assumes that the whole set 
of grapho-phonological regularities is captured 
through differentially weighted associations 
between letter coding and phoneme coding units 
of varying sizes (Seidenberg & McClelland, 1989; 
Plaut, Seidenberg, McClelland & Patterson, 
1996). 
These opposing theories have nourished an 
ongoing complex empirical debate for a number 
of years. This controversy constitutes one instance 
of a more general issue in cognitive science, 
which bears upon the proper explanation of rule- 
like behavior. Is the language user's capacity to 
exploit print-sound regularities, for instance to 
generate a plausible pronunciation for a new, 
unfamiliar string of letters, best explained by 
knowledge of abstract all-or-none rules, or of the 
436 
statistical structure of the language? We believe 
that, in the field of visual word processing, the 
lack of precise quantitative descriptions of the 
mapping system is one factor that has impeded 
resolution of these issues. 
In this paper, we present a descriptive analysis of 
the grapheme-phoneme mapping system of the 
French orthography, and we further explore the 
sensitivity of adult human readers to some 
characteristics of this mapping. The results 
indicate that human naming performance is 
influenced by the frequency of graphemic units in 
the language and by the predictability of their 
mapping to phonemes. We argue that these results 
implicate the availability of graded knowledge of 
grapheme-phoneme mappings and hence, that 
they are more consistent with a parallel distributed 
approach than with the abstract rules hypothesis. 
. Statistical analysis of grapho- 
phonological correspondences of 
French 
1.1. Method 
Tables of grapheme-phoneme associations 
(henceforth, GPA) were derived from a corpus of 
18.510 French one-to-three-syllable words from 
the BRULEX Database (Content, Mousty, & 
Radeau, 1990), which contains orthographic and 
phonological forms as well as word frequency 
statistics. As noted above, given that graphemes 
may consist of several letters, the segmentation of 
letter strings into graphemic units is a non-trivial 
operation. A semi-automatic procedure similar to 
the rule-learning algorithm developed by 
Coltheart et al. (1993) was used to parse words 
into graphemes. 
First, grapheme-phoneme associations are 
tabulated for all trivial cases, that is, words which 
have exactly the same number of graphemes and 
phonemes (i.e. PAR,/paR/). Then a segmentation 
algorithm is applied to the remaining unparsed 
words in successive passes. The aim is to select 
words for which the addition of a single new GPA 
would resolve the parsing. After each pass, the 
new hypothesized associations are manually 
checked before inclusion in the GPA table. 
The segmentation algorithm proceeds as follows. 
Each unparsed word in the corpus is scanned from 
left to right, starting with larger letter groups, in 
order to find a parsing based on tabulated GPAs 
which satisfies the phonology. If this fails, a new 
GPA will be hypothesized if there is only one 
unassigned letter group and one unassigned 
phoneme and their positions match. For instance, 
the single-letter grapheme-phoneme associations 
tabulated at the initial stage would be used to 
mark the P-/p/and R-/R/correspondences in the 
word POUR (/puRl) and isolate OU-/u/as a new 
plausible association. 
When all words were parsed into graphemes, a 
80 
70 
60 
50 
40 
30 
20 
10 
0 
Grapheme-Phoneme 
Association Probability 
Figure 1. Distribution of Grapheme-Phoneme Association 
probablity, based on type measures. 
70 Grapheme Entropy (H) 
60 ! Most unpredictable graphemes 
50 ! (H • .90) 
Vowels: e, oe, u, ay, eu, 'i 
40 Consonants: x, s, t, g, II, c 
3o 
20 
10 
o 
o ~ d o d o d o o d ........ 
Figure2. Dis~ibutionof~aphemeEn~y(H) values, 
b~on~eme~rcs. 
437 
Predictibility of Grapheme-Phoneme Associations in French 
GPA probability GPA probability H (type) H (token) 
(type) (token) 
Numberof 
pmnunci=ions 
M SD M SD M SD M SD M SD 
All 1.70 (1.26) .60 (.42) .60 (.43) .27 (.45) .23 (.42) 
Vowels 1.66 (1.12) .60 (.41) .60 (.44) .29 (.48) .21 (.41) 
Consonants 1.76 (1.23) .60 (.42) .60 (.42) .25 (.42) .26 (.44) 
Table I. Number of different pronunciations of a grapheme, grapheme-phoneme association (GPA) probability, and 
entropy (H) values, by type and by token, for French polysyllabic words. 
final pass through the whole corpus computed 
grapheme-phoneme association frequencies, based 
both on a type count (the number of words 
containing a given GPA) and a token count (the 
number of words weighted by word frequency). 
Several statistics were then extracted to provide a 
quantitative description of the grapheme-phoneme 
system of French. (1) Grapheme frequency, the 
number of occurrences of the grapheme in the 
corpus, independently of its phonological value. 
(2) Number of alternative pronunciations for each 
grapheme. (3) Grapheme entropy as measured by 
H, the information statistic proposed by Shannon 
(1948) and previously used by Treiman, 
Mullennix, Bijeljac-Babic, & Richmond-Welty 
(1995). This measure is based on the probability 
distribution of the phoneme set for a given 
grapheme and reflects the degree of predictability 
of its pronunciation. H is minimal and equals 0 
when a grapheme is invariably associated to one 
phoneme (as for J and/3/)- H is maximal and 
equals logs n when there is total uncertainty. In 
this particular case, n would correspond to the 
total number of phonemes in the language (thus, 
since there are 46 phonemes, max H = 5.52). (4) 
Grapheme-phoneme association probability, 
which is the GPA frequency divided by the total 
grapheme frequency. (5) Association dominance 
rank, which is the rank of a given grapheme- 
phoneme association among the phonemic 
alternatives for a grapheme, ordered by decreasing 
probability. 
1.2. Results 
Despite its well-known complexity and ambiguity 
in the transcoding from sound to spelling, the 
French orthography is generally claimed to be 
very systematic in the reverse conversion of 
spelling to sound. The latter claim is confirmed by 
the present analysis. The grapheme-phoneme 
associations system of French is globally quite 
predictable. The GPA table includes 103 
graphemes and 172 associations, and the mean 
association probability is relatively high (i.e., 
0.60). Furthermore, a look at the distribution of 
grapheme-phoneme association probabilities 
(Figure 1) reveals that more than 40% of the 
associations are completely regular and 
unambiguous. When multiple pronunciations exist 
(on average, 1.70 pronunciations for a grapheme), 
the alternative pronunciations are generally 
characterized by low GPA probability values (i.e., 
below 0.15). 
The predictability of GPAs is confirmed by a very 
low mean entropy value. The mean entropy value 
for all graphemes is 0.27. As a comparison point, 
if each grapheme in the set was associated with 
two phonemes with probabilities of 0.95 and 0.05, 
the mean H value would be 0.29. There is no 
notable difference between vowel and consonant 
predictability. Finally, it is worth noting that in 
general, the descriptive statistics are similar for 
type and token counts. 
2. Empirical study: Grapheme frequency 
and grapheme entropy 
To assess readers' sensitivity to grapheme 
frequency and grapheme entropy we collected 
naming latencies for pseudowords contrasted on 
those two dimensions. 
438 
I I • II I • 
Grapheme Frequency Grapheme Entropy 
Low High Low High 
Latencies 
Immediate Naming 609 (75) 585 (66) 596 (72) 644 (93) 
Delayed Naming 335 (42) 342 (53) 333 (51) 360 (54) 
Delta Scores 274 (94) 243 (84) 263 (94) 284 (105) 
Errors 
Immediate Naming 8.1 (7.0) 8.9 (5.8) 9.2 (4.7) 14.2 (7.3) 
Dela~ced Namin~ 2.7 ~3.41 3.9 ~5.7) 2.5 ~2.4 / 8.0 ~6.3 / 
Table 2. Average reaction times and errors for the grapheme frequency and grapheme entropy (uncertainty) 
manipulations (standard deviations are indicated into parentheses) in the immediate and delayed naming tasks. 
2.1. Method 
Participants. Twenty French-speaking students 
from the Free University of Brussels took part in 
the experiment for course credits. All had normal 
or corrected to normal vision. 
Materials. Two lists of 64 pseudowords were 
constructed. The first list contrasted grapheme 
frequency and the second manipulated grapheme 
entropy. The grapheme frequency and grapheme 
entropy estimates for pseudowords were 
computed by averaging respectively grapheme 
frequency or grapheme entropy across all 
graphemes in the letter string. Low and high 
values items were selected among the lowest 30% 
and highest 30% values in a database of about 
15.000 pseudowords constructed by combining 
phonotactically legal consonant and vocalic 
clusters. 
The frequency list comprised 32 pairs of items. In 
each pair, one pseudoword had a high averaged 
grapheme frequency, and the other had a low 
averaged grapheme frequency, with entropy kept 
constant. Similarly, the entropy list included 32 
pairs of pseudowords with contrasting average 
values of entropy and close values of average 
grapheme frequency. 
In addition, stimuli in a matched pair were 
controlled for a number of orthographic properties 
known to influence naming latency (number of 
letters and phonemes; lexical neighborhood size; 
number of body friends; positional and non 
positional bigram frequency; grapheme 
segmentation probability; grapheme complexity). 
Procedure. Participants were tested individually 
in a computerized situation (PC and MEL 
experimentation software). They were 
successively tested in a immediate naming and a 
delayed naming task with the same stimuli. In the 
immediate naming condition, participants were 
instructed to read aloud pseudowords as quickly 
and as accurately as possible, and we recorded 
response times and errors. In the delayed naming 
task, the same stimuli were presented in a 
different random order, but participants were 
required to delay their overt response until a 
response signal appeared on screen. The delay 
varied randomly from trial to trial between 1200 
and 1500 msec. Since participants are instructed 
to fully prepare their response for overt 
pronunciation during the delay period, the delayed 
naming procedure is meant to provide an estimate 
of potential artefactual differences between 
stimulus sets due to articulatory factors and to 
differential sensitivity of the microphone to 
various onset phonemes. 
Pseudowords were presented in a random order, 
different for each participant, with a pause after 
blocks of 32 stimuli. They were displayed in 
lower case, in white on a black background. In the 
immediate naming task, each trial began with a 
fixation sign (*) presented at the center of the 
screen for 300 msec. It was followed by a black 
screen for 200 msee and then a pseudoword which 
stayed on the screen until the vocal response 
triggered the microphone or for a maximum delay 
of 2000 msec. An interstimulus screen was finally 
presented for 1000 msee. In the delayed naming 
task, the fixation point and the black screen were 
439 
followed by a pseudoword presented for 1500 
msec, followed by a random delay between 1300 
and 1500 msec. After this variable delay, a go 
signal (####) was displayed in the center of the 
screen till a vocal response triggered the 
microphone or for a maximum duration of 2000 
msec. Pronunciation errors, hesitations and 
triggering of the microphone by extraneous noises 
were noted by hand by the experimenter during 
the experiment. 
2.2. Results 
Data associated with inappropriate triggering of 
the microphone were discarded from the error 
analyses. In addition, for the response time 
analyses, pronunciation errors, hesitations, and 
anticipations in the delayed naming task were 
eliminated. Latencies outside an interval of two 
standard deviations above and below the mean by 
subject and condition were replaced by the 
corresponding mean. Average reaction times and 
error rates were then computed by subjects and by 
items in both the immediate naming and the 
delayed naming task. By-subjects and by-items 
(Ft and F2, respectively) analyses of variance 
were performed with grapheme frequency and 
grapheme entropy as within-subject factors. 
Grapheme frequency. For naming latencies, 
pseudowords of low grapheme frequency were 
read 24 msec more slowly than pseudowords of 
high grapheme frequency. This difference was 
highly significant both by subjects and by items; 
Fj(1, 19) = 24.4, p < .001, Fe(1, 31) = 7.5, p < 
.001. On delayed naming times, the same 
comparison gave a nonsignificant difference of-7 
msec. For pronunciation errors, there was no 
significant difference in the immediate naming 
task. In the delayed naming task, pseudowords of 
low mean grapheme frequency caused 1.2% more 
errors than high ones. This difference was 
marginally significant by items, but not significant 
by subjects; F2(1, 31) = 3.1,p < .1. 
Grapheme entropy. In the immediate naming 
task, high-entropy pseudowords were read 48 
msec slower than low-entropy pseudowords; FI(1, 
19) = 45.4,p < .001, Fe(1, 31) = 16.2,p < .001. In 
the delayed naming task, the same comparison 
showed a significant difference of 27 msec; FI(1, 
19) = 22.9 p < .001, F2(1, 31) = 12.5, p < .005. 
Because of this articulatory effect, delta scores 
were computed by subtracting delayed naming 
times from immediate naming times. A significant 
difference of 21 msec was found on delta scores; 
FI(1, 19) = 5.7,p < .05, F2(1, 31) = 4.7,p < .05. 
The pattern of results was similar for errors. In the 
immediate naming task, high-entropy 
pseudowords caused 5% more errors than low- 
entropy pseudowords. This effect was significant 
by subjects but not by items; Ft(1, 19) = 7.4, p < 
.05, F2(1, 31) = 2.1,p > .1. The effect was of 6.5% 
in the delayed naming task and was significant by 
subjects and items; FI(1, 19) = 17.2, p < .001, 
F2(1, 31) = 8.3,p < .01. 
2.3. Discussion 
A clear effect of the grapheme frequency and the 
grapheme entropy manipulations were obtained 
on immediate naming latencies. In both 
manipulations, the stimuli in the contrasted lists 
were selected pairwise to be as equivalent as 
possible in terms of potentially important 
variables. 
A difference between high and low-entropy 
pseudowords was also observed in the delayed 
naming condition. The latter effect is probably 
due to phonetic characteristics of the initial 
consonants in the stimuli. Some evidence 
confirming this interpretation is adduced from a 
further control experiment in which participants 
were required to repeat the same stimuli presented 
auditorily, after a variable response delay. The 27 
msec difference in the visual delayed naming 
condition was tightly reproduced with auditory 
stimuli, indicating that the effect in the delayed 
naming condition is unrelated to print-to-sound 
conversion processes. Despite this unexpected 
bias, however, when the influence of phonetic 
factors was eliminated by computing the 
difference between immediate and delayed 
naming, a significant effect of 21 msec remained, 
demonstrating that entropy affects grapheme- 
phoneme conversion. 
These findings are incompatible with current 
implementations of the dual-route theory 
(Coltheart et aL, 1993). The "central dogma" of 
this theory is that the performance of human 
subjects on pseudowords is accounted for by an 
analytic process based on grapheme-phoneme 
conversion rules. Both findings are at odds with 
the additional core assumptions that (1) only 
440 
dominant mappings are retained as conversion 
rules; (2) there is no place for ambiguity or 
predictability in the conversion. 
In a recent paper, Rastle and Coltheart (1999) note 
that "One refinement of dual-route modeling that 
goes beyond DRC in its current form is the idea 
that different GPC rules might have different 
strengths, with the strength of the correspondence 
being a function'of, for example, the proportion of 
words in which the correspondence occurs. 
Although simple to implement, we have not 
explored the notion of rule strength in the DRC 
model because we are not aware of any work 
which demonstrates that any kind of rule-strength 
variable has effects on naming latencies when 
other variables known to affect such latencies 
such as neighborhood size (e.g., Andrews, 1992) 
and string length (e.g., Weekes, 1997) are 
controlled." 
We believe that the present results provide the 
evidence that was called for and should incite 
dual-route modelers to abandon the idea of all-or- 
none rules which was a central theoretical 
assumption of these models compared to 
connectionist ones. As the DRC model is largely 
based on the interactive activation principles, the 
most natural way to account for graded effects of 
grapheme frequency and pronunciation 
predictability would be to introduce grapheme and 
phoneme units in the nonlexical system. 
Variations in the activation resting level of 
grapheme detectors as a function of frequency of 
occurrence and differences in the strength of the 
connections between graphemes and phonemes as 
a function of association probability would then 
explain grapheme frequency and grapheme 
entropy effects. However an implementation of 
rule-strength in the conversion system of the kind 
suggested considerably modifies its processing 
mechanism, notably by replacing the serial table 
look-up selection of graphemes by a parallel 
activation process. Such a change is highly likely 
to induce non-trivial consequences on predicted 
performance. 
Furthermore, and contrary to the suggestion that 
the introduction of rule-strength would amount to 
a mere implementational adaptation of no 
theoretical importance, we consider that it would 
impose a substantial restatement of the theory, 
because it violates the core assumption of the 
approach, namely, that language users induce all- 
or-none rules from the language to which they are 
exposed. Hence, the cost of such a (potential) 
improvement in descriptive adequacy is the loss 
of explanatory value from a psycholinguistic 
perspective. As Seidenberg stated, "\[we are\] not 
claiming that data of the sort presented \[here\] 
cannot in principle be accommodated within a 
dual route type of model. In the absence of any 
constraints on the introduction of new pathways 
or recognition processes, models in the dual route 
framework can always be adapted to fit the 
empirical data. Although specific proposals might 
be refuted on the basis of empirical data, the 
general approach cannot." (Seidenberg, 1985, p. 
244). 
The difficulty to account for the present findings 
within the dual-route approach contrasts with the 
straigthforward explanation they receive in the 
PDP framework. As has often been emphasized, 
rule-strength effects emerge as a natural 
consequence of learning and processing 
mechanisms in parallel distributed systems (see 
Van Orden, Pennington, & Stone, 1990; Plaut et 
al., 1996). In this framework, the rule-governed 
behavior is explained by the gradual encoding of 
the statistical structure that governs the mapping 
between orthography and phonology. 
Conclusions 
In this paper, we presented a semi-automatic 
procedure to segment words into graphemes and 
tabulate grapheme-phoneme mappings 
characteristics for the French writing system. In 
current work, the same method has been applied 
on French and English materials, allowing to 
provide more detailed descriptions of the 
similarities and differences between the two 
languages. Most previous work in French (e.g. 
Vrronis, 1986) and English (Venezky, 1970) has 
focused mainly on the extraction of a rule set. One 
important feature of our endeavor is the extraction 
of several quantitative graded measures of 
grapheme-phoneme mappings (see also Bern&, 
Reggia, & Mitchum, 1987, for similar work in 
American English). 
In the empirical investigation, we have shown 
how the descriptive data could be used to probe 
human readers' written word processing. The 
results demonstrate that the descriptive statistics 
441 
capture some important features of the processing 
system and thus provide an empirical validation of 
the approach. Most interestingly, the sensitivity of 
human processing to the degree of regularity and 
frequency of grapheme-phoneme associations 
provides a new argument in favor of models in 
which knowledge of print-to-sound mapping is 
based on a large set of graded associations rather 
than on correspondence rules. 
Acknowledgements 
This research was supported by a research grant 
from the Direction Grn6rale de la Recherche 
Scientifique -- Communaut6 fran~aise de 
Belgique (ARC 96/01-203). Marielle Lange is a 
research assistant at the Belgian National Fund for 
Scientific Research (FNRS). 

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