Myanmar Number Normalization for Text-to-Speech
Aye Mya Hlaing, Win Pa Pa
Natural Language Processing Lab.,
University of Computer Studies, Yangon,
Yangon, Myanmar{ayemyahlaing, winpapa}@ucsy.edu.mm

Abstract---Text Normalization is an essential module for Text-to-Speech (TTS) system as TTS systems
need to work on real text. This paper describes Myanmar number normalization designed for Myanmar
Text-to-Speech system. Semiotic classes for Myanmar
language are identiﬁed by the study of Myanmar
text corpus and Weighted Finite State Transducers (WFST) based Myanmar number normalization
is implemented. Number suﬃxes and preﬁxes arealso applied for token classiﬁcation and ﬁnally, postprocessing has been done for tokens that cannot beclassiﬁed. This approach achieves average tag accuracy of 93.5% for classiﬁcation phase and average
Word Error Rate (WER) 0.95% for overall performance which is 5.65% lower than rule-based system.
The results show that this approach can be used in
Myanmar TTS system and to our knowledge, this is
the ﬁrst published work of Myanmar number normalization system designed for Myanmar TTS system.
Keywords-Myanmar Number Normalization; Text
Normalization; Weighted Finite State Transducer;
Myanmar Text-to-Speech; Myanmar;

I. Introduction
Text-to-Speech (TTS) synthesis is a technique forgenerating intelligible, natural-sounding artiﬁcial speech
for a given input text. Typical TTS systems have twomain components, text analysis and waveform synthesis[1]. Text normalization, phonetic analysis and prosodic
analysis are needed to be done in text analysis components. Text normalization is the ﬁrst and crucial phaseof text analysis.
TTS systems need to work on real text and it contains
non-standard words (NSWs), including numbers, dates,
currency amounts, abbreviations and acronyms. NSWs
cannot be found in a dictionary or cannot get theirpronunciations by "letter-to-sound" rules [2].
Myanmar text contains many NSWs with numbers.
If normalization of these NSWs is not performed in theinitial step of Myanmar TTS system, the quality of thesystem will be degraded. Therefore, normalization of
NSWs with Myanmar numbers are more emphasized forthis research.
Most text normalization still involves handconstructed grammars because statistical techniques

Ye Kyaw Thu
Artiﬁcial Intelligence Lab.,
Okayama Prefectural University,
Okayama, Japanye@c.oka-pu.ac.jp

typically require at least some annotated data to trainmodels, which is often not available for all of theambiguities one would like to resolve [3].
Some information of rule-based text normalization for
Myanmar language are described in [4]. Rule-based andlookup dictionary based methods are applied in text
normalization of Croatian [5], Bengali [6] languages.
Finite State Automata (FSA) and Maximum Entropy(ME) Classiﬁer and Rules are used as the text normalization strategy of Mandarin [7] language. Applicationof decision tree and decision list are explored in Hindi
text normalization [8]. Language model, supervised andunsupervised approaches are applied in Russian number names [9] and Vietnamese text normalization [10].
Weighted ﬁnite-state transducers (WFSTs) are applied
for the Kestrel text normalization system, a componentof the Google text-to-speech synthesis system [3]. Recurrent neural networks (RNN) are applied to learn thecorrect normalization function from a large corpus ofwritten text aligned to its normalized spoken form. Although RNN produces good results on overall accuracy,
it is prone to make the occasional errors [11].
For Myanmar language, statistical approaches cannot
be used on number normalization because there is no annotated or parallel normalized corpus for Myanmar number normalization. Therefore, Myanmar number normalization is implemented by writing number normalization
grammars that are compiled into libraries of WFST.
This paper describes the semiotic classes for Myanmar
language and the development of number normalization
for Myanmar text-to-speech system by implementing
two phases: classiﬁcation and verbalization phases. Word
segmentation process is needed as the pre-processing
step because Myanmar text lacks white space between
words. Language speciﬁc grammars based on WFST are
applied for this system.
II. Defining Semiotic Classes for Myanmar
Language
The ﬁrst part in number normalization is classiﬁcation of NSWs and the second part is verbalizing thedetected NSWs into their standard words. For NSWs

classiﬁcation, the semiotic classes for Myanmar language is needed to identify. Based on the investigationof Myanmar sentences from Asian Language Treebank(ALT) parallel corpus1 [12] and some Web data (3,150
sentences), we identiﬁed a set of semiotic classes for
Myanmar Language. ALT corpus is the parallel corpus
for seven languages in the news domain and comprises
20,000 sentences. The result semiotic classes are shownin Table I. NSWs (Myanmar digits) are shown in boldstyle in tables and contents of the paper.
Table I
Semiotic Classes for Myanmar
Semiotic Class

Description

DATE

date

၁၂-၂-၂၀၁၇,၂၀၁၇ ေဖေဖာ်ဝါရီ ၁၂, etc.

TIME

time

၁၀း၂၀း၂၅,၁၀ နာရီ ၂၀ မိ နစ်,ဂျီအမ်တီ ၀၈၃၀, etc.

CURRENCY

currency
amount၁၂၃,၄၅၀ ကျပ်,$၃,၀၀၀, etc.

NUMBER

cardinal,
decimal၁၀ ေယာက်,၁၂၅.၄၅,၆၀% , etc.

DIGIT

digit
digit

RANGE

range

၇၀ - ၈၀ ဒီဂရီ ဖာရင်ဟိက်,ေဒလာ ၁၀၀ ှ င့ ် ၁၅၀ ကား, etc.

SCORE

score

၂-၃ ဂိ း, ၂း၃ ဂိ း, etc.

DIMENSION

dimensionေပ ၄၀ × ၆၀, etc.

NRC

national
identiﬁcation
number၅/မရန(ိ င်) ၁၂၃၄၅၆, etc.

byExample:+၉၅-၉ ၄၃၀၅၄၄၈၅,အမှတ် ၄၄၇, etc.

RE for detecting currency with currency suﬃx iswritten as follows:
if($inputdata =~ m/([၀-၉]{1,3},([၀-၉]{3},)*[၀-၉]{3})(\.?[၀-၉]*\s*)($strCurrencySuf)/g)

This rule-based number normalization system is used
as a baseline in this paper.
IV. Weighted Finite State Transducer for
Number Normalization
The main point of this paper is language-speciﬁcgrammar that is compiled to WFST. A WFST consistsof a set of states and transitions between states. Eachtransition is labeled with an input symbol from an input
alphabet; an output symbol from an output alphabet;an origin state; a destination state; and a weight [13].
Finite State Transducer (FST) can represent certain
sets and binary relations over string. A Deterministic
Finite Automaton recognizes a regular language and a
FST recognizes a regular relation. FSTs have been used
to model the morphology, phonology and other textanalysis operations such as numeral expansion. WFSTs
have been used in Text analysis model for Text-toSpeech (TTS) of the multilingual Bell Labs TTS system[14]. Kestrel text normalization system, a component of
the Google text-to-speech synthesis system, also used
grammars based on WFSTs for text normalization ofmany languages [3].
OpenGrm Thrax Grammar Compiler2 [13] is used
for the development of Myanmar number normalization
system. The Thrax grammar compiler compiles grammars that consist of regular expressions, and contextdependent rewrite rules, into FST archives of weighted
ﬁnite state transducers. It uses OpenFst library toprovide an eﬃcient encoding of grammars and generalalgorithms.

III. Defining Rules for Number Normalization

V. Myanmar Number Normalization

For detection of the semiotic classes deﬁned in Table I, 60 rules are deﬁned for simple rule-based number
normalization system. Rule-based number normalization
is implemented by regular expression (RE) in Perl language. Look-up dictionary is used for expansion of measurement units and currency unit symbols. An example
RE for detecting date range in DATE semiotic class is
as follows:

Myanmar number normalization is achieved by implementing two phases : classiﬁcation and verbalization phases. Both phases are accomplished by deﬁning
grammars that are compiled to WFST. Semiotic classes
deﬁned in Section II is used for classiﬁcation. Number
preﬁxes and suﬃxes are also used as the clues for
identifying semiotic class of the current word. Word
segmentation process is needed as the pre-processing
step of this system. Detailed processes for the wholesystem are discussed in the following sections.

if($inputdata =~ m/(\s+[၀-၉]{4}\s*)-(\s*[၀-၉]{4}\s*)($strTwoDateSuf)/g)
1 availableat
http://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/index.html

2 available at http://www.openfst.org/twiki/bin/view/GRM/
Thrax

A. Grammars for Classiﬁcation
Grammars that are compiled to the libraries of WFST
are applied for classiﬁcation phase. Before applying
these grammars, some pre-processing rules are written
for combining tokens. Some digit sequences are needto combined for classifying their semiotic classes. For
example, in Myanmar sentence,"မီ တာ ၁၀၀ ၊ ၂၀၀ ၊ ၃၀၀ စီ ေဝးပါတယ်။"(Each fars 100, 200, 300 meters.)
In this case, "မီ တာ" (meter) is the clue for identifying
the whole digit sequence as the NUMBER class and it islocated before the ﬁrst digit. The other digits need to berelated that clue. Spaces between these digit sequences
are removed as the pre-processing step and it becomes"မီ တာ ၁၀၀၊၂၀၀၊၃၀၀ စီ ေဝးပါတယ်။". Therefore, the whole digit
sequence can be identiﬁed as the NUMBER class.
For DATE semiotic class, more than one rule isneeded to implement because many patterns of DATE
are commonly written in Myanmar. Table II shows someexamples of Myanmar date.
Table II
Myanmar Dates

The ﬁrst example is ordinal number using ordinary
number that means "second" in English and the second
one is using text that means "ﬁrst" in English. Therefore,
it is no need to identify as one type of semiotic class.
Measure unit symbols, 96 number preﬁxes and 275 number suﬃxes are used for classiﬁcation of NUMBER semiotic class. These preﬁxes and suﬃxes are extracted bymanual collecting of Myanmar text corpus. Measure unit
symbols with their expansions like "% (ရာခိ င်န်း)", "°F(ဒီဂရီ ဖာရင်ဟိက်)", "ft (ေပ)" are applied by using StringFile
function.
Finally, grammars for all deﬁned semiotic classes arecompiled to WFSTs and then these WFSTs are exported
and used in Classiﬁcation phase. Weights are assigned fordeﬁning diﬀerent priorities of the classiﬁcation. Example
input string and output string of classiﬁcation phase areas follows:
Input String: ၂၀၁၇ ဧပီ လ ၁၇ ရက်သည် ြမန်မာှ စ်ဆန်းတစ်ရက်ေန့ ြဖစ်ပါသည်။

(17th April, 2017 is Myanmar New Year's day.)Output String : <DATE>year: ၂၀၁၇ month: ဧပီ လ day:၁၇</DATE> ရက်သည် ြမန်မာ ှ စ်ဆန်းတစ်ရက်ေန့ ြဖစ်ပါသည် ။
B. Grammars for Verbalization

MyanmarEnglishဇန်နဝါရီ ၄၊ ၂၀၁၇January 4 2017၂၀၁၇ ၊ ဇန်နဝါရီ လ ၄2017 January 4၂၀၁၇ ခှ စ် ၊ ဇန်နဝါရီ လ ၄2017 January 4၂၀၁၇ ၊ ၄ လပိ င်း

4/2017

ဇန်နဝါရီ လ ၄January 4

၄.၁.၂၀၁၇

4.1.2017

၄-၁-၂၀၁၇

4-1-2017

၄/၁/၂၀၁၇

4/1/2017

၁၃၇၈ ခှ စ် ဝါဆိ လဆန်း ၁၀ရက်

10, early Warso, 1378

၁၃၇၈ ခှ စ်၊ တန်ေဆာင်မန်း
လြပည့ ်ေကျာ် ၁၀ ရက်

10, late
1378

၂၀၁၆-၂၀၁၇ ခှ စ်

2016-2017

၂၀၁၆-၂၀၁၇ ပညာသင်ှစ်

2016-2017 academic year

Tazaungmone,

There are two types of clues in classiﬁcation for CURRENCY semiotic class. It can be used currency symbolas the preﬁx of the number or currency text as the preﬁx
or suﬃx of the number. As an example, "$ ၅၀","ေဒလာ
၅၀" and "၅၀ ေဒလာ" has the same meaning in English
as "$50".
In this paper, cardinal, decimal and measure aredeﬁned in the member of NUMBER semiotic class. In
Myanmar, ordinal numbers are usually written by using
ordinary number or text.
Examples are "၂ ကိ မ်ေြမာက်" (second) and "ပထမအကိ မ်"(ﬁrst).

Verbalization is the expansion of tokens into standard
words and its expansion mainly depends on its semiotic
class. Verbalization of semiotic classes that come out
from classiﬁcation phase is also done by using WFST. In
Myanmar language, some symbols have to be neglected
in verbalization. For example, in DIMENSION semiotic
class,"ေပ ၄၀ × ၆၀" (40 ft × 60 ft) can be expanded into"ေပ ေလး ဆယ် ေြခာက် ဆယ်".
In this case, "×" symbol must be neglected in verbalization.
A goal score, "၃-၁ ဂိ း" (3:1) can be expanded into"သံး ဂိ း တစ် ဂိ း".
In the above SCORE case, "-" symbol needs to be
replaced by suﬃx "ဂိ း". In Myanmar, "-" is usually usedin SCORE case.
Myanmar people sometimes omit "တစ်" (one) in pronunciation of Myanmar digit sequence like "တစ်ဆယ့ ်"(ten) and "တစ်ေထာင့ ်" (one thousand). Examples are:၁၁း၀၀ နာရီ (11:00 hour)- ဆယ့ ် တစ် နာရီ၁၉၁၅ (1915)- ေထာင့ ် ကိ း ရာ့ ဆယ့ ် ငါး၁၅၀၀ ကျပ် (1500 kyats)- ေထာင့ ် ငါး ရာ ကျပ်Therefore, some rules for ﬁxing these particular cases
are written in verbalization grammars.
C. Myanmar Number Names Expansion
All the grammars except digit by digit expansion depend on the expansion of Myanmar number names. The
number name grammars depend on the factorizationof digit string into sum of products of powers of ten.

These factorizations can be done by using Thrax Factorizer grammar. Number verbalization of these factorized
strings into number names depends on the language.
Most Western languages have no terms for 104 , they say
ten thousand. The factorization becomes 1×101 ×103 .
In Myanmar language, we have a term "ေသာင်း" (tenthousand) for 104 and the factorization is 1×104 . Although there are terms "သန်း" (million) for 106 and"ကေဋ" (ten million) for 107 in Myanmar language, it
is not commonly used in pronunciation of CURRENCY
class. Myanmar people usually say "၁၀ သိ န်း" (ten lakh)for 106 and "သိ န်း ၁၀၀" (hundred lakh) for 107 . Therefore,
factorization deﬁnes 1×101 ×105 for the ﬁrst case and1×102 ×105 for the second case and Myanmar number
names grammar has to be handled these factorizations
into appropriate Myanmar standard words. As an example,"၁,၂၃၄,၅၆၇,၈၉၀" (1,234,567,890) will be expanded into
standard words as"တစ် ေသာင်း ှ စ် ေထာင့် သံး ရာ့ ေလး ဆယ့် ငါး သိ န်း ေြခာက် ေသာင်း
ခနစ် ေထာင့် ှစ် ရာ့ ကိ း ဆယ်".
(twelve thousand three hundred forty-ﬁve lakh sixtyseven thousand eight hundred and ninety)
D. Finalization
After verbalization phase, some digit sequences are
missing to identify their semiotic classes and to verbalize
their standard words because they have no clue fordeﬁning semiotic classes. In ﬁnalization, post-processing
have been done for ﬁxing these cases. Some assumptions
are made to decide whether current token should beclassiﬁed as cardinal number or as digit by digit. If
there is a comma or dot in digit sequence or it has one
non-zero digit followed by many zero digits, it will be
expanded into cardinal number names. If not, how many
digits in the sequence will be checked. If it has threeor more digits, it will be pronounced as digit by digit
and if it has less than three digits, pronounced as the
cardinal number. Myanmar people usually pronounce
as that way. For example, Bus no. "၃၉" (39) is usuallypronounced as "သံး ဆယ့ ် ကိ း" (thirty-nine) and Bus no.
"၁၂၄" (124) as "တစ် ှ စ် ေလး" (one two four).
VI. Sample Grammar Fragments
In classiﬁcation phase, as we mentioned in Section V-A, some clues are used for classifying semiotic
class. However, if there is a number sequence of same
semiotic class, it is common that clue is located at thestart or end of the sequence of numbers separated by
Myanmar punctuation "၊". As an example, a sequence of
currency number is written as"၂၀,၀၀၀ ၊ ၁၅,၀၀၀ ၊ ၁၀,၀၀၀ ကျပ်" (20,000 , 15,000 , 10,000
kyats) instead of writing "၂၀,၀၀၀ ကျပ်၊ ၁၅,၀၀၀ ကျပ်၊ ၁၀,၀၀၀ကျပ်" (20,000 kyats, 15,000 kyats, 10,000 kyats).

Example grammar for detecting sequence of currency
is as follows:
For example : ၂၀,၀၀၀ ၊ ၁၅,၀၀၀ ၊ ၁၀,၀၀၀ ကျပ် => <CURRENCY>integer: ၂၀,၀၀၀ ၊ integer: ၁၅,၀၀၀ ၊ integer: ၁၀,၀၀၀
currencySuﬃx: ကျပ် </CURRENCY>
currency sequence =u.Ins["<CURRENCY>"]
u.Ins[" integer: "]validnum(u.D["."]
u.Ins[" fractional part: "]d+)? "၊"(u.Ins[" integer: "]validnum(u.Del["."]
u.Ins[" fractional part: "]d+)? "၊" )*u.Ins[" integer: "]validnum(u.Del["."]
u.Ins[" fractional part: "]d+)?
u.Del[" "*]u.Ins[" currencySuﬃx: "]currency suﬃx
u.Ins["</CURRENCY>"]

In verbalization, Myanmar number name grammar isused as the basic and some rewrite rules are written to ﬁx
some particular cases. For example, omitting the pronunciation of "တစ်" before "ဆယ့ ်" and "ေထာင့ ်" is written byusing content-dependent rewrite rule and composition isapplied in this case. Example code fragment is as follows:
For example : ၁၁-၃-၁၉၁၇ (11-3-1917) => ဆယ့ ် တစ် ရက် သံးလ ေထာင့် ကိ း ရာ့ ဆယ့ ် ခနစ်
day = n.MYANMAR NUMBER NAME;
month num = n.MYANMAR NUMBER NAME;
year = n.MYANMAR NUMBER NAME;
date num =u.Del["<DATE>"]
u.Del[" day: "]day
u.Ins[" ရက် " ]u.Del[" month: "]month numu.Ins[" လ "]u.Del[" year: "]year
u.Del["</DATE>"];
remove tit = CDRewrite["တစ်" : "" , "", (" ဆယ့် ") | ("ေထာင့ ် "), sigma star];export DATE VERBALIZE = Optimize[date num @remove tit];

VII. Test Data Preparation
Two test data TestData-1 and TestData-2 are prepared for evaluating current WFST-based Myanmar
number normalization. TestData-1 has 1,000 sentences
which are randomly selected from ALT corpus thatneed to be normalized. For TestData-2, some Myanmar
sentences from the Web that contains 947 sentences with
Myanmar digits are selected. For these two test data,
parallel tagged corpus is prepared for evaluating classiﬁcation and parallel normalized corpus for evaluating theoverall performance.
VIII. Experimental Results
Experiments are designed to test the performanceof classiﬁcation and verbalization of Myanmar number
normalization.
A. Classiﬁcation
The following formula is used for evaluating the performance of classiﬁcation.
number of particular tagsin test data
tag accuracy(%) =(1)number of particular tagsin ref erence data
Table III shows the number of particular tags in test data
and reference data, and the percentage of tag accuracy.
The overall tag accuracy is 94.3% on TestData-1 and92.6% on TestData-2.
Table IIIClassification Accuracy of Two Test Data
Semiotic ClassNUMBERDATETIMECURRENCY
DIGIT
RANGE
SCORE
DIMENSION
Overall Accuracy

TestData-1
755/810 (93.2%)
396/404 (98%)
78/80 (97.5%)
71/82 (86.6%)
5/5 (100%)
2/2 (100%)
5/8 (62.5%)
1312/1391 (94.3%)

TestData-2
725/789 (91.9%)
422/442 (95.5%)
68/68 (100%)
98/118 (83.1%)
3/3 (100%)
6/8 (75%)
2/2 (100%)1324/1430 (92.6%)

B. Verbalization
Verbalization results is reported in terms of word error
rate (WER), a standard for evaluation of Automatic
Speech Recognition system. Simple rule-based number
normalizer developed by Perl language is used as thebaseline in this paper. In Table IV, current number
normalization system based on WFST achieves WER
0.5% for TestData-1 and 1.4% for TestData-2, andwhich is 5.0% and 6.3% lower than the baseline system
respectively.
Table IV
Overall Performance

Baseline
WFST-based

TestData-1 (WER%)
5.5%
0.5%

TestData-2 (WER%)
7.7%
1.4%

IX. Error Analysis and Discussion
Some errors are found in classiﬁcation phase in our
experiments because of two factors. The ﬁrst factor isthat there is no clue before and after the digit sequence.
In this example, ၇၀ ကေန ၃၁၀ ကီ လိမီ တာ (from 70 to 310
kilometers) should be classiﬁed as<NUMBER>၇၀</NUMBER> ကေန <NUMBER>
integer: ၃၀၀ numberSuﬃx: ကီ လိမီ တာ</NUMBER>.
However the classiﬁer cannot classify which classshould be occupied "၇၀" because it has no clue.
The second factor is that there are some common
preﬁxes or suﬃxes on both CURRENCY and NUMBER
classes. For example, in this Myanmar sentence,"ကမာ့လ ဦးေရက လတိ င်း ၆ သန်း ေလာက်တိးပွ ားေနပါတယ်။"(The world's population has increased about 6 million
per month.),although "၆ သန်း" (6 million) should be classiﬁed as the
NUMBER class, its result class is CURRENCY because"သန်း" (million) is also the preﬁx of CURRENCY class.
As the same way, CURRENCY class is sometimes
misclassiﬁed as NUMBER class. For example, in thesentence,"အေမရိ ကန်ေဒလာ သန်းေပါင်း ၃၈၀ ခန့် ရင်းှ ီးမပ်ှံမည်"(They will be invested approximately 380 million dollars.),
the preﬁx of the digit sequence "သန်းေပါင်း" is the preﬁxof CURRENCY class and the suﬃx "ခန့်" is the suﬃx
used in NUMBER class. Therefore, classiﬁer based on
FST chose the shorter path "၃၈၀ ခန့်" as the NUMBER
class.
Although there are some classiﬁcation errors, verbalization results of some misclassiﬁcation have also correct
pronunciation because almost same pronunciation for
CURRENCY and NUMBER class in Myanmar language and post-processing of our number normalization.
Therefore, low WER on overall accuracy is achieved inour experiments.
X. Conclusion and Future Work
Number normalization is a very important module in
Text-to-Speech system. In this paper, semiotic classes
for Myanmar language are identiﬁed and WFST-based
Myanmar number normalization designed for Myanmar
TTS system is implemented for the ﬁrst time. Experimental results show that this WFST-based approach can
get acceptable results for the performance of Myanmar
number normalization and this can be used practicallyby integrating in Myanmar TTS system.
For solving misclassiﬁcation and missing tag cases inclassiﬁcation phase, this phase will be replaced by applying sequence-to-sequence modeling. A tagged corpora
for various types of data resources will be built for thatmodelling.

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