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Proceedings of the Conference on Language & Technology 2009
English to Urdu Transliteration System
Abbas Raza Ali and Madiha Ijaz
Center for Research in Urdu Language Processing,
National University of Computer and Emerging Sciences, Lahore, Pakistan
{abbas.raza, madiha.ijaz}@nu.edu.pk
Abstract English text discards it and as a result generated
speech or translation lacks coherence.
Urdu language processing applications encounter English to Urdu transliteration system is being
non-Urdu text specifically English text frequently. The developed to eradicate this discrepancy as shown in
accuracy of these systems e.g. machine translation, Table 1.
text-to-speech etc. is highly undermined as they are
unable to handle English text. One possibility could be Table 1: Effect of transliteration on Urdu TTS
addition of multilingual language processing system
capabilities in Urdu language processing applications
Alex Nokiað
Urdu Text
so that they may handle English text also along with ö
Urdu but this approach is quite taxing. Another َ
approach to handle English text is to transliterate it With
ð
into Urdu automatically and then pass it on to the Transliterati
Urdu language processing applications. on ö
This paper describes English to Urdu Without
transliteration system. First the mapping rules that are Transliterati ö! "
ð
used to generate Urdu text from English transcription
on
are discussed then syllabification, manual
transliteration and Urduization phase is described and In order to develop English to Urdu transliteration
finally the issues related to Out-Of-Vocabulary (OOV) system, first the rule-based approach employing
are discussed. transliteration from English orthography to Urdu
orthography was explored but soon it was realized
1. Introduction that it would not work well as there is no one-to-one
mapping between English orthography and its
Transliteration is a method of transcribing the corresponding sound e.g. // sound is represented
words from one script to phonetically equivalent words using six different letter combinations i.e. motion
in another script. Transliteration rules provide /mo.n/, ocean /o.n/, sure /.r/, she /i/,
mapping for the letters of the source script alphabet to admission /æ .m
.n/, machine /m.in/ etc [2]. So
the letter of target script alphabet based on phonetic
similarity. This process is very successful for pronunciation based transliteration was chosen as it
transliteration of names of people, places, companies, produced better results.
etc., because the translated dictionaries can never be An Arpabet based English pronunciation lexicon
comprehensive and are ineffective for translation of is used for acquiring pronunciation of English words.
proper noun [1]. English text is converted to Urdu using English
Websites, user interfaces etc. contain a lot of pronunciation and mapping rules. The English
English text along with Urdu text. The text is read by pronunciation lexicon is based on American accent,
the applications like screen reader, web page reader hence the transliteration into Urdu also depicts
etc. and is passed to the Urdu language processing American accent. Frequently used English words are
application e.g. machine translation for translation or transliterated manually and some rules are applied for
text-to-speech system for speech generation. Urdu TTS Urduization of the transliterated text in order to make
or machine translation system being unable to handle it appropriate and as close as possible to the local
15
Proceedings of the Conference on Language & Technology 2009
accent i.e. the accent that is used in Pakistan while Some sounds have multiple realizations in Urdu
speaking English. orthography e.g. /s/ can be realized as %،$،# etc
Out-Of-Vocabulary problem is resolved using
statistical techniques by first aligning English so in this case only one most commonly used alphabet
orthography to pronunciation sequences. Optimal is chosen which is $ in this case. Similar is the case
pronunciation of an unknown word is computed by
picking maximum probable pronunciation and then with /z/ as it can be realized as (،'،،& and /t/
passing it for the same transliteration process. which can be realized either as ) or *.
The architecture of the English to Urdu
Transliteration system is shown in figure 1. Vowels in Urdu are represented using diacritics
i.e. zair, zaber and paish and four letters alif, wao,
English Text choti yeh and bari yeh. Combination of diacritics with
consonants forms short vowels while diacritics
Converted to Load combined with alif, wao, choti yeh and bari yeh, form
transliteration long vowels [3]. Same vowel is represented
English OOV and language
model differently in orthography, depending on whether it
exists word initially, medially or finally.
applying Computing
Short vowels occurring word initially use alif as
Syllabification Optimal َ -َ
place holder e.g. “urban” is transliterated to +
Pronunciation ,
applying Sequence /’r.bn/ but when they occur word medially they are
represented only by the diacritics e.g. “justly” is
Urduization َ
transliterated to .12ð /’s.li/.
0 ,
Converted ِ
Short vowels when occur word finally are
transformed into their corresponding long vowel i.e.
Urdu Script zabar is converted to alif e.g. “Andorra” /Ænd /
َ َ َ
is transliterated to -345 /æn.’ .r /, similarly zair is
Figure 1: Architecture of English-to-Urdu
transliteration converted to choti yeh and pesh is converted to wao.
Hence there is no one-to-one correspondence
2. English to Urdu mapping between English and Urdu vowels in most of the
cases and an English vowel is transliterated using
CMU pronouncing dictionary (v 0.7a) is used to multiple Urdu characters depending on whether it
acquire pronunciation of English words. The occurs word initially, medially or finally as shown
dictionary comprises of 125,000 English words and table 2.
their corresponding transcription in Arpabet. The Table 2: English vowels mapping to Urdu
pronunciation provided is based on American accent orthography
[11]. Urdu
Arpabet IPA Initial Middle Final
The phonemic inventory of English comprises of َ َ
24 consonants and 15 vowels. The phonemic inventory AA 6 7 7
of Urdu comprises of 37 consonants and 16 vowels َ َ
(Appendix B). English consonants can be easily AE Æ 8 87 ے
mapped to Urdu consonants and there is one-to-one : َ : َ
AY A
6 7;7 7
correspondence between them in all cases. There are . ِ .
some sounds in English e.g. dental fricatives, /Θ/ and ِ َ َ
AW A ؤ6 ؤ7 ؤ7
/Ð/ which are non-existent in Urdu and hence they are َ َ َ
mapped to their closest counterpart i.e. dental stops /t / AO 3 37 37
and /d/ respectively. OY
7;6 7;3 =
ِ َ ِ َ
EH " 8 87 ے
16
Proceedings of the Conference on Language & Technology 2009
َ َ َ .so.i. َ َ َ
ER # - -7 -7 Associate ? @ A ? BC A
t َ
EY E
8 8 ے â
b.l
.vi. َ َ 83.G
Oblivious D E ِ َ
IH
7 7 s ِ ,
ِ ِ ِ $
IY I 8 87 87
ِ ِ ِ 8ڈ I3
o.bi.di. َ ِ .
Obedient 3 ,ِ
?5 4 َ
OW O 3 3 3 nt , ?J
ُ ُ ُ
UH 7 7
َ َ َ
AH 7 7 3.2. Special case
After applying syllabification there exists
problem of local accent, as transliteration is based on
3. Syllabification American accent so in order to make transliteration
closer to Urdu accent some rules are applied on
English-to-Urdu transliteration using CMU syllabified transliterated text.
Pronunciation dictionary which is based on American
accent, generates a lot of inconsistency. To improve 3.2.1. Consonant Cluster. Urdu syllabification does
system’s accuracy; Urdu syllabification is applied on not allow consonant cluster in onset of the syllable
English transcription as shown in table 3. and in the word medial position. In this case add /
/ if
Consonant and Vowels combine to make syllable the second consonant is ‘r’ or ‘l’ otherwise //
and breaking up a word into syllables is known as
syllabification. Sonority sequence principle for between two consonants and mark syllable boundary
syllabification is commonly used in Urdu. It requires after it as shown in table 4.
the onset to rise in sonority towards the nucleus and Table 4: Examples of consonant cluster
codas to fall in sonority from the nucleus [9]. problem
English IPA Urdu IPA
3.1 Algorithm َ
K L
Treehoppe t Ih p ِ َِ t
. I.h .p#
Template matching technique is used to syllabify r #
English transcription. In this technique syllabication is َ M
done by matching template of the form [4]. Bless bl"s b
.l"s
C0,1.V.Cn َ,ِ
Urdu allows only one consonant in the onset position "
. N
Quickly kw
kli 0 k .w
k.li
and multiple consonants can come in the coda position ِ ِ
of the syllable.
1. Convert the entire phonemic transcription of the 3.2.2. Urduization. If two consonants come in the
word to consonant-vowel pairs onset of the syllable in the word initial position and
2. Start from the end of the word, traverse backwards the starting consonant is ‘s’ then add // before ‘s’ as
to find the next vowel
3. repeat shown in table 5.
4. if there is a consonant preceding it
5. mark a syllable boundary before consonant Table 5: Examples of Urduization applied on
6. else transliterated Urdu text
7. mark the syllable boundary before this vowel
8. end if English IPA Urdu IPA
9. until the entire string is consumed School skul O ُP
s.kul
ِ
Table 3: Transliteration after applying Skill sk
l QR
s.k
l
syllabification َ َِ ِ
Special sp" l QS
s.p". l
English IPA Urdu M ِ
Unsyllabified Syllabified
17
Proceedings of the Conference on Language & Technology 2009
4. Out-Of-Vocabulary problem ^ (1)
P =argmaxp(P | E ) = argmax p(E | P)p(P)
r r n n r r
P P
r r
Out-Of-Vocabulary is a very common problem in The trigram language model p(P .P .P ) and
r r r
various systems like text-to-speech, machine i−1 i i+1
bigram transliteration model p(E .P ) is combined to
n r
translation, cross language information retrieval i i
(CLIR), etc. To resolve this problem, English phoneme maximize the pronunciation probabilityP .
r
to orthography alignment has to be found out
probabilistically to get one-to-one mapping between 4.2. Computing optimal pronunciation
them as shown in table 6, and then train those aligned sequence
sequence to get most probable pronunciation for an
unknown word. Expectation maximization algorithm is used to
compute optimal alignment sequence. The algorithm
Table 6: English orthography to pronunciation is given below;
alignment Initialization
English Percentages For each English phoneme to orthography pair, assign
Pronunciatio p . er . s . eh . n . t . ih . jh . ah . z equal weights to all possibilities generated from (1).
n repeat
Alignment p(p) . er(er) . c(s) . e(eh) . n(n) . t(t) . Expectation-Step
a(ih) . g(jh) . e(ah) . s(z) For each of the Arpabet phonemes, count up
The entire procedure consists of two steps; instances of its different mappings from the
· English orthography to pronunciation alignment. observations on all combinations produced in (1).
· Computing optimal pronunciation sequence. Normalize the score so that the mapping
After getting pronunciation of unknown text, it will probabilities sum to 1.
be passed through the same procedure like Maximization-Step
syllabification and then Urdu transliteration. The Recalculate the combination scores. Each
architecture of the OOV module is shown in figure 2. combination is scored with the product of the scores
of the symbol mappings it contains. Normalize the
CMU pronunciation dictionary scores so that the mapping probabilities sum to 1.
until convergence
English orthography to phoneme alignment 5. Results
Transliteration process becomes more accurate
after applying syllabification on the pronunciation
Pronunciation parameter estimation and finding probabilistic sequences of Out-Of-
Vocabulary word problem as shown in figure 3.
English-to-Urdu Mappings
Pronunciation parameter optimization To achieve more accuracy
Syllabification
Bigram transliteration model and trigram
language model probabilities To achieve more accuracy
Figure 2: Architecture of English-to-Urdu Urduization Rules
transliteration
To enhance overall capability
4.1. Orthography to pronunciation alignment
Out-Of-Vocabulary
In this step all the valid combinations of English
orthography E to its pronunciation sequence P are Figure 3: Modules of the system that lead it
n r towards maturity
produced using conditional probability;
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