Multilingual Text Analysis for TexttoSp eech

Synthesis

Richard Sproat

Sp eech Synthesis Research Department

Bell Lab oratories Lucent Technologies

Mountain Avenue Ro om d

Murray Hill NJ USA

rwsbelllabscom

In many TTS systems the rst three tasks word seg Abstract We present a mo del of text analysis for textto

mentation and digit and abbreviation expansion would sp eech TTS synthesis based on weighted nitestate trans

b e classed under the rubric of text normalization and would ducers which serves as the textanalysis mo dule of the mul

generally b e handled prior to and often in a quite diererent tilingual Bell Labs TTS system The transducers are con

fashion from the last two problems which fall more squarely structed using a lexical to olkit that allows declarative de

within the domain of linguisti c analysis scriptions of lexicons morphologica l rules numeralexpansion

One problem with this approach is that in many cases the rules and phonologi cal rules inter alia To date the mo del

selection of the correct linguisti c form for a normalized item has b een applied to eight languages Spanish Italian Roma

cannot b e chosen b efore one has done a certain amount of lin nian French German Russian Mandarin and Japanese

guistic analysis Consider an example that is problematic for

the Bell Lab oratories American English TTS system a system

Intro duction

that treats text normalization prior to and separately from

The rst task faced by any texttosp eech TTS system is

the rest of linguisti c analysis If one encounters the string

the conversion of input text into an internal linguisti c repre

$ in an English text the normal expansion would b e ve

sentation This is in general a complex task since the written

dol lars But this expansion is not always correct when func

form of any language is at b est an imp erfect representation

tioning as a prenominal mo dier as in the phrase $ bil l the

of the corresp onding sp oken forms Among the problems that

correct expansion is ve dol lar since in general plural noun

one faces in handling ordinary text are the following

forms cannot function as mo diers in English The analysis

of complex noun phrases in the American English system cf

While a large numb er of languages delimit words using

comes later than the prepro cessing phase and since a

whitespace or some other device some languages such as

hard decision has b een made in the earlier phase the system

Chinese and Japanese do not One is therefore required

pro duces an incorrect result

to reconstruct word b oundaries in TTS systems for these

An even more comp elling example can b e found in Russian

languages

While in English the p ercentage symb ol when denoting a

Digit sequences need to b e expanded into words and more

p ercentage is always read as percent in Russian selecting the

generally into wellformed numb er names so in En

correct form dep ends on complex contextual factors The rst

glish would generally b e expanded as two hundred and forty

decision that needs to b e made is whether or not the numb er

three

p ercent string is mo difying a following noun Russian in gen

Abbreviations need to b e expanded into full words In gen

eral disallows nounnoun mo dication in constructions equiv

eral this can involve some amount of contextual disam

alent to nounnoun comp ounds in English the rst noun must

biguation so kg can b e either kilogram or kilograms de

b e converted into an adjective thus rog rye but rzhanoj xleb

p ending up on the context

ryeadj bread rye bread This general constraint applies

Ordinary words and names need to pronounced In many

equally to procent p ercent so that the correct rendition of

languages this requires morphological analysis even in lan

skidka twenty p ercent discount is dvadcatiprocentnaja

guages with fairly regular sp elling morphological struc

skidka twenty p ercentadj discount

gen nomsgfem nomsgfem

ture is often crucial in determining the pronunciation of a

Note that not only do es procent have to b e in the adjectival

word

form but as with any Russian adjective it must also agree

Proso dic phrasing is only sp oradicall y indicated by punc

in numb er case and gender with the following noun Observe

tuation marks in the input and phrasal accentuation is

almost never indicated At a minimum some amount of

But see which treats numeral expansion as an instance of

lexical analyis in order to determine eg grammatical part

morphological analysis and also the work of van Leeuwen

of sp eech is necessary in order to predict which words to

which uses cascaded rewrite rules within his To o iP system to

L

make prominent and where to place proso dic b oundaries wards the same ends

c

R Sproat

Pro ceedings of the ECAI Workshop

Extended Finite State Models of Language

Edited by A Kornai

also that the word for twenty must o ccur in the genitive

Overall Architecture

case In general numb ers which mo dify adjectives in Russian

Let us start with the example of the lexical analysis and pro

must o ccur in the genitive case consider for example etazh

nunciation of ordinary words taking again an example from

storey and dvuxetazhnyj two storeyadj

gen nomsgmasc

Russian Russian orthography is often describ ed as morpho

If the p ercentage expression is not mo difying a following noun

logical meaning that the orthography represents not a

then the nominal form procent is used However this form ap

surface phonemic level of representation but a more abstract

p ears in dierent cases dep ending up on the numb er it o ccurs

level This is description is correct but from the p oint of view

with With numb ers ending in one including comp ound num

of predicting word pronunciation it is noteworthy that Rus

b ers like twenty one procent o ccurs in the nominative sin

sian with a welldened set of lexical exceptions is almost

gular After socalled paucal numb ers two three four and

completely phonemic in that one can predict the pronuncia

their comp ounds the genitive singular procenta is used Af

tion of most words in Russian based on the sp elling of those

ter all other numb ers one nds the genitive plural procentov

words provided one knows the placement of word stress

So we have odin procent one p ercent dva procenta

nomsg

since several Russian vowels undergo reduction to varying de

two p ercent and pyat procentov ve p ercent

gensg genpl

grees dep ending up on their p osition relative to stressed syl

All of this however presumes that the p ercentage expression

lables The catch is that word stress usually dep ends up on

as a whole is in a nonoblique case If the expression is in

knowing lexical prop erties of the word includin g morphologi

an oblique case then b oth the numb er and procent show up

cal class information To take a concrete example consider the

in that case with procent b eing in the singular if the num

word kostra Cyrillic ko stra b onregenitivesi ngul ar This

b er ends in one and the plural otherwise s odnym procen

word b elongs to a class of masculine nouns where the word

tom with one p ercent with one p ercent

instrsgmasc instrsg

stress is placed on the inectional ending where there is one

s pjatju procentami with ve p ercent with

instrpl instrpl

0

Thus the stress pattern is kostr a and the pronunciation is

ve p ercent As with the adjectival forms there is nothing

0

kstr with the rst o reduced to Let us assume that

p eculiar ab out the b ehavior of the noun procent all nouns ex

the morphological representation for this word is something

hibit similar b ehavior in combination with numb ers cf

0

like kostrfnoungfmascgfinang afsggfgeng where for con

The complexity of course arises b ecause the written form

venience we represent phonological and morphosyntactic in

gives no indication of what linguisti c form it corresp onds to

formation as part of the same string Assuming a nite

Furthermore there is no way to correctly expand this form

state mo del of lexical structure we can easily imag

without doing a substantial amount of analysis of the context

ine a set of transducers M that map from that level into a

includi ng some analysis of the morphological prop erties of the

level that gives the minimal morphologicall ymoti vated an

surrounding words as well as an analysis of the relationship

notation MMA necessary to pronounce the word In this

of the p ercentage expression to those words

0

case something like kostr a would b e appropriate Call this

The obvious solution to this problem is to delay the deci

lexicaltoMMA transducer L such a transducer can b e

w or d

sion on how exactly to transduce symb ols like in English

constructed by comp osing the lexical acceptor D with M so

or in Russian until one has enough information to make

that L D M A transducer that maps from the MMA

w or d

the decision in an informed manner This suggests a mo del

to the standard sp elling kostra ko stra would among other

where say an expression like in Russian is transduced

things simply delete the stress marks call this transducer S

into all p ossible renditions and the correct form selected from

The comp osition L S then computes the mapping from

w or d

the lattice of p ossibili ties by ltering out the illegal forms

the lexical level to the surface orthographic level and its in

An obvious computational mechanism for accomplishin g this

verse L S S L computes the mapping from

w or d

w or d is the nitestate transducer FST Indeed since it is well

the surface to all p ossible lexical representations for the text

known that FSTs can also b e used to mo del most morphol

word A set of pronunciatio n rules compiled into a transducer

ogy and phonology as well as to segment words

cf and b elow P maps from the MMA to the surface

in Chinese text and as we shall argue b elow for p er

phonologic al representation note that by starting with the

forming other textanalysis op erations such as numeral ex

MMA rather than with the more abstract lexical representa

pansion this suggests a mo del of textanalysis that is entirely

tion the pronunciation rules do not need to duplicate informa

based on regular relations We present such a mo del b elow

tion that is contained in L anyway Mapping from a single

w or d

More sp ecically we present a mo del of text analysis for TTS

orthographic word to its pronunciation thus involves comp os

based on weighted FSTs WFSTs which serves as the

ing the acceptor representing the word with the transducer

textanalysis mo dule of the multilingu al Bell Labs TTS sys

S L L P or more fully as S M D M P

w or d

tem To date the mo del has b een applied to eight languages

w or d

For textual elements such as numb ers abbreviations and

Spanish Italian Romanian French German Russian Man

sp ecial symb ols such as the mo del just presented seems

darin and Japanese One prop erty of this mo del which distin

less p ersuasive b ecause there is no asp ect of a string such as

guishes it from most textanalyzers used in TTS systems is

that indicates its pronunciation such strings are purely

that there is no sense in which such tasks as numeral expan

logographic some might even argue ideographic repre

sion or wordsegmentation are logicall y prior to other asp ects

senting nothing ab out the phonology of the words involved For

of linguistic analysis and there is therefore no distingui shed

textnormalization phase

Conversion b etween a attened representation of this kind and

a hierarchical representation more in line with standard linguistic

mo dels of morphology and phonology is straightforward and we

will not dwell on this issue here

This is certainly not completely true in all such cases as mixed

representations such as st and nd suggest But such cases are

Multilingual Text Analysis for TTS Synthesis R Sproat

these cases we presume a direct mapping b etween all p ossi

or set of transducers P to the lexical analysis or more prop

ble forms of procent and the symb ol call this transducer

erly to the lexical analysis comp osed with the lexicaltoMMA

L a subset of L Then L maps from the

map M as we saw ab ove Although the lexicaltoMMA map

per c special sy mbol

per c

symb ol to the various forms of procent In the same way

M was intro duced as mapping from the lexical analyses of

L the transducer L maps from numb ers followed

ordinary words to their MMA if the map is constructed with

per c

number s

by the sign for p ercent into various p ossible and some im

sucient care it can serve as the transducer for lexical anal

p ossible lexical renditions of that string the various forms

yses coming from any of the textword mo dels

to b e disambiguated using contextual information as we shall

show later on Abbreviations are handled in a similar manner

The To ols

note that abbreviations such as kg kg in Russian show the

same complexity of b ehavior as procent

The construction of the WFSTs dep ends up on a lexical to olkit

So far we have b een discussing the mapping of single text

lextools that allows one to describ e linguisti c general

words into their lexical renditions The construction of an

izations in linguisti cal ly sensible humanreadable form The

analyzer to handle a whole text is based on the observation

to olkit has more or less the same descriptive p ower as the

that a text is simply constructed out of zero or more instances

Xerox to ols though the current version of lexto ols lacks

of a text word coming from one of the mo dels describ ed ab ove

some of the debugging capabiliti es of the Xerox system and

ie either an ordinary word an abbreviation a numb er a

the Xerox to ols do not allow costs in the descriptions whereas

sp ecial symb ol or p ossibly some combination of numb ers with

lexto ols do es

a sp ecial symb ol with each of these tokens separated by some

Some of the to ols do not require much comment for read

combination of whitespace or punctuation The structure of

ers familiar with previous work on nitestate phonology and

this mo del of lexical analysis is summarized in Figure

morphology In addition to some basic to ols to deal with ma

We presume two mo dels for space and punctuation The

chine lab els there is a to ol compw l that compiles lists of

mo del L maps b etween interword and its p otential lexical

strings or more general regular expressions into nitestate

realizations usually a word b oundary but in some cases a

lexicons a to ol paradigm to construct inected morpholog

higherlevel proso dic phrase b oundary Interword is parame

ical forms out of inectional paradigm descriptions and lex

terized so that in Europ ean languages for example it corre

icons that mark the paradigm aliation of stems a to ol for

sp onds to actual whitespace whereas in Chinese or Japanese

constructing nitestate word grammars arclist the name

it corresp onds to Similarl y the mo del L maps b etween

punc

b eing inspired by and a rewrite rule compiler based

punctuation marks p ossibly with anking whitespace and

on the algorithm describ ed in

the lexical realization of those marks in many though not

The to ol numbuilder constructs the transducer L

number s

all cases the punctuation mark may corresp ond to a proso dic

which converts strings of digits up to a userdened length

phrase b oundary

into numb ernames appropriate for that string The construc

The output of the lexical analysis WFST diagramed in

tion factors the problem of numeral expansion into two sub

Figure is a lattice of all p ossible lexical analyses of all

problems The rst of these involves expanding the numeral

words in the input sentence Obviously in general we want to

sequence into a representation in terms of sums of pro ducts

remove contextually inapp opriate analyses and to pick the

of p owers of the base usually ten call this the Factoriza

b est analysis in cases where one cannot make a categori

tion transducer The second maps from this representation

cal decision This is accomplished by a set of one or more

into numb er names using a lexicon that describ es the map

language model transducers henceforth which are de

ping b etween basic numb er words and their semantic value in

rived from rules and other linguistic descriptions that apply

terms of sums of pro ducts of p owers of the base call this the

to contexts wider than the lexical word Phrasal accentua

Number Lexicon transducer A sample Numb er Lexicon frag

tion and proso dic phrasing are also handled by the language

ment for German is shown in Figure The rst stage of the

mo del transducers The output of comp osing the lexical anal

expansion is language or at least languagearea dep endent

ysis WFST with is a lattice of contextually disambiguated

since languages dier on which p owers of the base have sepa

lexical analyses The b estpath of this lattice is then selected

rate words see inter alia so Chinese and Japanese have

using a Viterbi best path algorithm Costs on the lattice may

a separate word for whereas most Europ ean languages

b e costs handselected to disfavor certain lexical analyses

lack such a word The full numeral expansion transducer is

see the Russian p ercentage example detailed in a subsequent

constructed by comp osing the Factorization transducer with

section or they may b e genuine dataderived cost estimates

the transitive closure of the Numb er Lexicon transducer In

as in the case of the Chinese lexical analysis WFST where the

some languages additional manipulation s are necessary and

costs corresp ond to the negative log unigram probability of

these involve the insertion of a sp ecial lter transducer b e

a particular lexical entry Given the b est lexical analy

tween the Factorization and Numb er Lexicon transducers In

sis one can then pro ceed to apply the phonological transducer

German for example the words for decades come after the

most easily treated as also b eing logographic at least in the

The work is also similar to the To o iP to olkit for linguistic rules

L

present architecture

for TTS discussed in However the latter do es not compile

To date our multilingual systems have rather rudimentary lexical the rules into WFSTs Instead the right and left contexts are

class based accentuation rules and punctuationbased phrasing compiled into an FSMlike format which is then used to match

Thus these comp onents of the systems are not as sophisticated these contexts for the rules at runtime Note also that unlike the

as the equivalent comp onents of our English system current system which compiles linguistic descriptions into WFSTs

This is largely b ecause the relevant research has not b een done that allow multiple outputs To o iP functions in a completely

L

for most of the languages in question rather than for technical deterministic fashion in that for any given input there can only

problems in tting the results of that research into the mo del b e one output

Multilingual Text Analysis for TTS Synthesis R Sproat



S L L

punc

w or d

L L

special sy mbol

L

number s

Figure Overall structure of the lexical analysis p ortion Note that corresp onds to whitespace in German or Russian but in

Chinese or Japanese

words for units so b ecomes vierunddreiig

States Arcs

fourandthirty thus suggesting the order This

German

reversal can b e accomplished by a lter transducer which for

Russian

lack of a b etter name we will call Decade Flop The construc

Mandarin

tion of the numeral expander for German is shown schemati

Spanish

cally in Figure Note that the insertion of und in examples

like vierunddreiig thirty four is not actually accomplished

by numbuilder such clean up op erations can b e handled us

Table Sizes of lexical analysis WFSTs for selected languages

ing rewrite rules

One to ol that has so far not b een applied in the system is

the decision tree compiler describ ed in We are hoping

Size and Sp eed Issues

to apply this in the future for among other things phrasing

mo dels of the kind discussed in

Table gives the sizes of the lexical analysis WFSTs for

the languages German Spanish Russian and Mandarin To

a large extent these sizes accord with our intuitions of the

Russian Percentages

diculties of lexical pro cessing in the various languages So

Russian is very large correlating with the complexity of the

Let us return to the example of Russian p ercentage terms As

morphology in that language German is somewhat smaller

sume that we start with a fragment of text such as s skidkoi

Mandarin has a small numb er of states correlating with the

s skidkoj with discount with a vep ercent discount

fact that Mandarin words tend to b e simple in terms of mor

This is rst comp osed with the lexical analysis WFST to pro

phemic structure but there are a relatively large numb er of

duce a set of p ossible lexical forms see Figure By default

arcs due to the large character set involved Sizes for the

the lexical analyzer marks the adjectival readings of with

Spanish transducer are misleading since the current Spanish

meaning that they will b e ltered out by the language

system includes only minimal morphological analysis note

mo del WFSTs if contextual information do es not save them

though that morphologica l analysis is mostly unnecessary in

Costs on analyses here represented as subscripted oating

Spanish for correct word pronunciatio n

p oint numb ers mark constructions usually oblique case

While the transducers can b e large the p erformance on an

forms that are not in principle illformed but are disfavored

SGI Indy or Indigo is acceptably fast for a TTS applicatio n

except in certain welldened contexts The correct analysis

Slower p erformance is certainly observed however when the

b oxed in Figure for example has a cost of which is an

system is required to explore certain areas of the network

arbitrary cost assigned to the oblique instrumental adjectival

as for example in the case of expanding and disambiguati ng

case form the preferred form of the adjectival rendition

Russian numb er expressions

is masculine nominative singular if no constraints apply to

To date no formal evaluations have b een p erformed on the

rule it out

correctness of wordpronunciation in the various languages we

Next the language mo del WFSTs are comp osed with

are working on largely b ecause there is still work to b e done

the lexical analysis lattice The WFSTs include transducers

b efore the systems can b e called complete An evaluation of

compiled from rewrite rules that ensure that the adjectival

the correctness of word segmentation in the Mandarin Chinese

rendition of is selected whenever there is a noun following

system is rep orted in

the p ercent expression and rules that ensure the correct case

numb er and gender of the adjectival form given the form of the

Summary and Future Work

 

following noun In addition a lter expressable as

The system for text analysis presented in this pap er is a com removes any analyses containing the tag See Figure

plete working system that has b een used in the development The b estcost analysis among the remaining analyses is then

of working textanalysis systems for several languages In ad selected Finally the lexical analysis is comp osed with M P

dition to German Spanish Russian and Mandarin a system to pro duce the phonemic transcription see Figure

Multilingual Text Analysis for TTS Synthesis R Sproat

fg einsfnumgfmascgjfneutgfsg gfg

fg einefnumgffemigfsggfg

fg zweifnumgfg

fg dreifnumgfg

fgfgfgfg zehnfnumgfg

fgfgfgfg elffnumgfg

fgfgfgfg zwolffnumgfg

fgfgfgfg dreifgzehnfnumgfg

fgfg zwanfgzigfnumgfg

fgfg dreifgigfnumgfg

fg hundertfnumgfg

fg tausendfnumgfneutgfg

Figure German numb er lexicon The cost of on the feminine form eine eectively disfavors this form so that it will only b e

selected in appropriate predened contexts

Factorization )

DecadeFlop )



Numb erLexicon +

zweihundertvierunddreiig

Figure Expansion of in German using numbuilder

machine This has two imp ortant consequences First of all for Romanian has b een built and work on French Japanese

for a strictly nite state machine it is not necessary to ex and Italian is underway From the p oint of view of previous re

plicitly construct the machine b eforehand and this in turn search on linguisti c applications nitestate transducers some

implies that one can avoid precompilin g very large FSMs so asp ects of this work are familiar some less so Familiar of

long as one can provide an algorithm for constructing the ma course are application s to morphology phonology and syn

chine on the y One example is in discourse analysis where tax though most previous work in these areas has not made

one wants to rememb er which words or lemmata one has al use of weighted automata More novel are the applications

ready seen as previous work on accenting suggests this to text prepro cessing in particular numeral expansion and

kind of information is useful for TTS and is in fact used in word segmentation

the American English version of the Bell Labs synthesizer From the p oint of view of textanalysis mo dels for textto

In theory assuming the set of words or morphological stems sp eech the approach is quite novel since as describ ed in the

is closed one could construct an FSM that would remem intro duction most previous work treats certain op erations

b er when it had seen a word needless to say such a machine such as word segmentation or numeral expansion in a prepro

would b e astronomical in size and so the precompilation of cessing phase that is logicall y prior to the linguisti c analysis

this machine is out of the question one could however envision phase we have argued here against this view

dynamicall y buildin g states that rememb er that a particular Two areas of future work b oth dep end up on an imp or

word has b een seen Secondly one can in principle construct tant prop erty of the FSM to olkit on top of which the lex

GSMs which have greater than nitestate p ower again pro to ols to olkit is built Underlying the notion of an FSM is the

viding that one can sp ecify an algorithm for constructing on more general notion of a generalized state machine GSM

the y the arcs leaving a given state One obvious example An imp ortant prop erty of GSMs is that it is not necessary to

is a copy machine which will recognize which strings from a know b eforehand which arcs leave a given state rather one

lattice have the prop erty that they are of the form w w for can construct just the arcs one needs on the y as one is us

some string w this problem comes up in the analysis of mor ing the machine for example in a comp osition with another

Multilingual Text Analysis for TTS Synthesis R Sproat

s skidkoi

Lexical Analysis WFST

s p jat pro centn a ja skidk o j

prep num nom adj femsgnom fem sginstr

s p jat i pro centn o j skidk o j

prep num gen adj femsginstr fem sginstr

s p jat ju pro cent ami skidk o j

prep num instr noun plinstr fem sginstr

Figure Comp osition of s skidkoi s skidkoj with a discount with the lexical analysis WFST to pro duce a range of

p ossible lexical renditions for the phrase By default the adjectival readings of are marked with which means that they will b e

ltered out by the languagemo del WFSTs see Figure The b oxed analysis is the correct one Costs on analyses mark constructions

usually oblique case forms that are not in principle illformed but are disfavored except in certain welldened contexts

 

pro cent

noun

noun

 

pro centn

adj

noun

 

pro centn

Case\:instr

instr

 

pro centn

sgCase

pl

 

BestPath

s p jati pro centn o j skidko j

gen adj sginstr

Figure A subset of the language mo del WFSTs related to the rendition of p ercentages The rst blo ck of rules ensures that

adjectival forms are used b efore nouns by switching the tag on the adjectival and nominal forms The second blo ck of rules deals with

adjectival agreement with the adjectival forms The nal blo ck is a lter ruling out the forms tagged with The correct output of

this sequence of transductions is shown at the b ottom

phological reduplication Precompiling such a machine as an

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Acknowledgments

bridge University Press Cambridge

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Multilingual Text Analysis for TTS Synthesis R Sproat

s skidkoi

s p jati pro centn o j skidko j

gen adj sginstr

M P

+

s PiTprcEntny sKtky

Figure Mapping the selected lexical analysis to a phonetic rendition via comp osition with P

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Multilingual Text Analysis for TTS Synthesis R Sproat