FOR THE PHONEMICIZATIONOF ORTHOGRAPHY
Telesensory Speech Systems
University of North Carolina
Palo Alto, CA 94304
Chapel H i l l , NC 27514
A system for converting English text
into synthetic speech can be divided into two
processes that operate in series:
One fairly standard approach to
automatic phonemiczation of words has the
following component parts:
I) a text-to-phoneme converter, and
2) a phonemic-input speech synthesizer.
The conversion of orthographic text into a
phonemic form may itself comprise several
processes i n series, for instance, formatting
t e x t t o expand a b b r e v i a t i o n s and
n o n - a l p h a b e t i c e x p r e s s i o n s , p a r s i n g and word
of words, word and clause level stress
assignment, word internal and word boundary
allophonic adjustments, and duration and
fundamental frequency settings for
Comparing t h e accuracy of different
algorithms for text-to-phoneme conversion is
often difficult because authors measure and
report system performance in incommensurable
ways. Furthermore, comparison of the output
speech from two complete systems may not
always provide a good test of the performance
of the corresponding component algorithms in
the two systems, because radical performance
differences in other components can obscure
small differences in the components of
interest. The only reported direct comparison
of two complete text-to-speech systems (MITALK
and TSI's TTS-X) was conducted by Bernsteln
and P i s o n l ( 1 9 8 0 ) . This p a p e r r e p o r t s one
study t h a t compared two algorithms for
automatic segmental phonemlcization of words,
and a second study that compared two
algorithms for automatic assignment of lexical
Several research systems are of this general
design, including Allen's MITALK system, the
TTS-X prototype at Telesensory Systems, and
Llberman,s proper name p h o n e m i c i z e r .
The most popular text-to-phoneme
[email protected] is the NRL approach, which has only two
components, of which only the first is
presented in detail and evaluated by Elovitz.
The original NRL system is:
Only three systems for text-to-phoneme
conversion have been reported in detail:
McIlroy's (197~) Votrax driver, Hunnicutt,s
(1976) rules for the MITALK system, and the
NRL rules developed by Elovitz and associates
(1976). Liberman (1979), Hertz (1981), and
Hunnicutt (1980) have described more recent
systems, but have not published rule sets.
i n c l u d i n g some
and any a l l o p h o n i c s
was " d o e s t h i s
any a c c e p t a b l e
pronunciation o f the spelled word, assuming
one can assign stress correctly and then
reduce vowels ~ppropriately."
phonemlcization consistent wlth a n y possible
word class f o r t h a t spelling, o r any 'regular'
regional pronunciation was to be accepted.
Three judges (two phonetlcians and a
were given printed
copies of the
were i n fairly t r a n s p a r e n t
form. The judges chose among three possible
r e s p o n s e s t o each w o r d :
1 = correct;
close or questionable; and 0 = wrong. Cross
judge consistency can be seen from the bimodal
distribution of summed scores in Figure I.
The v e r y g r e a t a d v a n t a g e o f t h e MRL
approach, is the unified
and w h o l e w o r d s .
e x a c t l y one pass t h r o u g h a w o r d , l e f t
right, in which the maximum string starting
with the leftmost unphonemicized character is
These strings are sometimes whole
words, sometimes affixes, and sometimes
consonant or vowel sequences or word fragments
The m a i n constraint of the
system is its greatest attraction: the unity
and simplicity of the code that scans the word
and accesses a single table of letter strings.
In contrast to this, the MITALK system, for
instance, has one module and associated table
structure for lexlcal decomposition of whole
words, another module for stripping common
affixes, and a third m o d u l e for translating
consonant and vowel sequences that remain in
the pseudo-root of the word.
Fla I E t
Study One reports a comparison o f two
routines for translating orthographic letters
i n t o s e g m e n t a l p h o n e m e s : [email protected] and
[email protected] is the affix stripper
and letter to sound rules as dlacribed in AJCL
Microfiche 5 7 , and i m p l e m e n t e d i n MACRO-11 in
Telesensory Systems' TTS-X prototype
text-to-speech system. Hunnlcutt's system was
modified only slightly i n translation, and
about 20 rules were added. The system starts
from the right end of the word and identifies
as many suffixes as it can from a table of
a b o u t 140 s u f f i x e s ,
proceeding toward the
b e g i n n i n g o f t h e word u n t i l
o f t h e w o r d has no
vowel or fewer than three letters,
o r no m o r e
can be m a t c h e d .
Next, a similar
proceedure works from the beginning of the
word, matching as many prefixes as it can from
• a t a b l e o f a b o u t 40 p r e f i x e s .
pseudo-root of the word is scanned left
right twice, o n c e translating the c o n s o n a n t s ,
and next translating the vowels.
£ o, . ¢ ,LJ
[email protected] is a system implemented by
M a r t i n Minnow a t D i g i t a l
E q u t p t m e n t C o r p . The
whole system is somewhat more elaborate that
the original NRL system, but the letter to
sound m o d u l e and its mode o f o p e r a t i o n
basically as described b y Elovitz et alla,
with 20 or 30 rules added. The NRL rules
i n c l u d e a b o u t 60 v e r y common w h o l e w o r d s , as
w e l l as a b o u t 25 r u l e s t h a t h a n d l e v a r i o u s
environments for three prefixes
and f i f t e e n
A set of 865 words was processed both
by the [email protected] affix stripper and letter
to sound rules, and by the [email protected] letter to
sound rules including the a f f i x
rules and the
The 865 words comprised
approximately every fiftieth word o f the Brown
Corpus (Kucera & Francis,
1967) i n f r e q u e n c y
from about the 400th most
frequent word: "position."
The lexicon of the
TSI s y s t e m was d i s a b l e d ,
and none o f t h e w h o l e
words i n the NRL rules was i n the set of 865.
Since the output from both subsystems
was tapped before stress assignment, vowel
two stress levels. Hunnicutt also added stress
rules that depended on the occurance of
certain classes of suffixes. Hunnicutt's rules
require several pointers and a suffix table,
they sometimes pass through a word several
times in the manner of Chomsky & Halle's
(1968) rules, and they occupy about 3K bytes
of executable code in their TSI version.
Another, more diagnostic way to view
the results is to present the number of words
that fall into each cell of a 2X2 grid formed
by the [email protected] r a t i n g vs. the [email protected]
rating, as shown in Figure 2. Figure 2 omits
the 26 words that had a summed score of 1.5
for either of the two letter to sound systems.
The second algorithm is a simplified
version of a stress rule proposed in Hill &
We will refer to this rule as
Nessly's default, since it is the default case
of Nessly's full stress algorithm. Nessly's
default stress is quite similar to Latin
stress and to the "first approximation" stress
rule discussed twoard the beginning of Chomsky
& Halle's chapter three (1968, pp.69-77). The
main differences between Nessly's default rule
and Chomsky & Halle's "first approximation"
If the rule sets were equivalent, the
grid would have zeroes in cells b and c. If
one rule set were a super-set of the other,
you would get a zero in cell b or cell c, but
Most of the 553 words in cell d are
regular, or else are common exceptions (like
Most of the 127 words in cell a are
obviously exceptional (e.g. "minute, honor,
(I) No word class information is used
in Nessly's default, so verbs a r e stressed as
and (2) What constitutes a "strong
cluster" (which contains a tense vowel or a
closed syllable end) is different.
default is indifferent to vowel length or
Examination of the 159 words
distributed between cells b and c yields the
Of the 69 words that [email protected]
got right and [email protected] missed, nearly half are
correct by virtue of the extensive affix
stripping in Hunnicutt's algorithm.
these 69 words in cell c are "mobile, naval,
wallace, likened, coworkers, & reenacted."
Nessly's default rule can be outlined
If(number of syllables : I)
if(number of syllables : 2)
stress left syllable.
Of the 90 words that [email protected] got right
and [email protected] got wrong, only about 15 are
definitely due to NRL's word fragment rules.
Six of the 90 words are in cell d just because
NRL does not Strip suffixes the way that
Hunnicutt's rules do. These six words are
"november, visited, preferably, presidency,
september, & oven."
skip the last syllable.
stress third from last.
In general, both algorithms get about
25% wrong on this lexically flat sample of 865
About 15~ of the words a r e
incorectly phonemicized by both subsystems.
This might suggest that 15~ wrong may be a
state of the art performance level for
segmental phonemicization of word types by
sets of 400 rules.
(place alternating 2nd stresses
on syllables to the left.)
The MACRO-It version of this rule requires
about 150 bytes of executable code, and
accepts one pointer to the last vowel in the
It passes through the word once, right
to left, and it does very well assigning
correct stresses (in caps) to "LUminant" vs.
"maLIGnant," for example.
Study Two compared the performance of
two algorithms for assignment of lexical
stress to words.
Both of the algorithms were
coded in MACRO-It and ran in different
versions of TSl's TTS-X prototype
text-to-speech system. The first algorithm is
Hunnlcutt's lexical stresser, which is
described in detail in AjCL Microfiche 57.
Hunnicutt's algorithm is an adaptation of
Halle's cyclic stress rules for English. The
adaptations include adjustments for the less
specified input to the rules (e.g. the part of
speech of the root is unknown), and the number
of stress levels specified in the output is
reduced, presumably because the Klatt
synthesizer it was designed to drive only used
For testing the stress algorithms, a
sample of 430 words was selected. These 430
words were all the items of five or more
characters that had frequencies of 40 ppm
through 34 ppm (inclusive) in the Brown
corpus. The segmental phonemicization was done
by Hunnicutt's rules in TSI's TTS-X prototype.
The automatically produced segmental
phonemicizations that the stress algorithms
operated on were rejected only if they did not
have the correct number of syllables.
Thirteen of the 430 words were phonemicized
with the wrong number of syllables. Another
54, or 13~, of the 430 were one syllable
words, which were allways assigned correct
stress. Stress assignments were judged by the
The results on the remaining
417 words of the sample were:
Nessly d e f a u l t
J . B e r n s t e i n & D . P i s o n i (1980) " U n l i m i t e d
e v a l u a t i o n o f a m i c r o p r o c e s s o r based d e v i c e , "
IEEE ICASSP-80 Proceedings.
N.Chomsky & M.Halle (1968) THE SOUND PATTERN
OF ENGLISH, Harper-Row, New York.
H°Elovltz, R.Johnson, A.McHuKh, & J.Shore
(1976) "Letter-to-sound rules for automatic
of English text to phonetics,"
IEEE T r a n s . on A c o u s t i c s , Speech, and S i g n a l
P r o c e s s i n g , v o l . ASSP-24, no. 6 .
So, on these words, the two algorithms perform
at about the same level of accuracy, which is
about 252 wrong on a lexlcal sample.
S . H e r t z ( 1 9 8 1 ) "SRS l e t t e r
t o sound r u l e s , "
IEEE ICASSP-80 P r o c e e d i n g s .
In both studies, very simple
algorithms performed about as well as
algorithms of vastly greater complexity.
the case of the letter-to-sound algorithms
([email protected] and [email protected]), the difference in
complexity is primarily in the procedure for
checklnK the rules against the word.
Hunnicutt's rules themselves are only a little
more complicated than the NRL rules.
Presumably, with some modification, most of
Hunnicutt's rules could be modified to run
within a one-pass NRL procedure.
S.Hunnicutt (1976) "Phonological
rules for a
AJCL M i c r o f i c h e
( 1 9 8 0 ) "Grapheme t o phoneme r u l e s :
a r e v i e w " KTH SLT-QPSR 2 - 3 / 1 9 8 0 , S t o c k h o l m .
H . K u c e r a & W . F r a n c l s ( 1 9 6 7 ) COMPUTATIONAL
ANALYSIS OF PRESENT DAY AMERICAN ENGLISH,
Brown U. P r e s s , P r o v i d e n c e .
M.Llberman (1979) "Text-to-speech
" b y r u l e and a p r a c t i c a l
Proceedings of the Ninth International
C o n g r e s s o f P h o n e t i c S c i e n c e s , Copenhagen.
The stress algorithms tested in Study
Two p r e s e n t a v e r y g r e a t c o n t r a s t i n b o t h
number o f r u l e s and p r o c e d u r e f o r r u l e
application. If Nessly's default rule is llke
a simplified version of Chomsky & Halle's
a p p r o x i m a t i o n " s t r e s s r u l e , and l [
Hunnlcutt's algorithm is fairly close t o
Chomsky & Halle's full lexical stress rules
(with noun-root assumed), then our data
suggest that the epicyclic
p r o d u c e d Chomsky & H a l l e ' s f u l l
set of stress
rules from their "first approximation" has
gained almost nothing in lexical
M.McIlroy (197~) "Synthetic
E n g l i s h s p e e c h by
Bell Telephone Laboratories
& L . N e s s l y ( 1 9 7 3 ) " R e v i e w o f The Sound
Patten of English,"
LINGUISTICS 106: 5 7 - 1 0 1 .
We have reported performance in terms
of percent wrong on samples of word types from
the Brown corpus.
It seems that an
a p p r o p r i a t e measure of performance t h a t
what p e o p l e f e e l when t h e y h e a r a
text-to-speech system is AVERAGE WORDS BETWEEN
ERRORS (AWBE). We w o u l d like t o end this p a p e r
by g i v i n g AWBE f o r a s i m p l e t e x t - t o - p h o n e m e
system with a 25~ error rate i n both
letter-to-sound conversion and l e x l c a i
stressing, and a lexicon with 1500 words.
If the lexicon is in parallel
t o sound and s t r e s s r u l e s , and t h e
performance of the letter
t o sound r u l e s and
t h e s t r e s s r u l e s a r e i n d e p e n d a n t , an o v e r a l l
e r r o r r a t e o f a b o u t 7% can be e x p e c t e d .
i n t o an AWBE o f 1 3 . 3 .
The a u t h o r s g r a t e f u l l y
h e l p f r o m M a r t i n Minow,
P e t e r MaKEs,
M a r g a r e t Kahn, and o u l i e L o v i n = .