Download English to Sanskrit Translator and Synthesizer (ETSTS)

International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 2, Issue 12, December 2012)
English to Sanskrit Translator and Synthesizer (ETSTS)
Sarita G. Rathod1, Shanta Sondur2
1,2
Information Technology Department, VESIT, Mumbai University, Maharashtra, India
In this we use the dictionary rule based approach of
machine translation for translator of English to Sanskrit and
formant synthesis method for converting text into speech.
If we compare the grammar for both English and
Sanskrit then English sentences are always in order of
subject-verb-object format while Sanskrit has free word
order. For e.g. the order of English sentence (ES) and its
equivalent translation in Sanskrit sentence (SS) is given
below.
ES: He read book (SVO)
SS: Saha pustkam
pathati (SOV)
OR
Pustkam Saha Pathati (OSV)
OR
Pathati Pustkam Saha
Thus Sanskrit sentence can be written using SVO, SOV
and VOS order.
Speech is the primary means of communication between
people. Speech synthesis, automatic generation of speech
waveforms .Synthesis procedure consists of two main
phases. The first one is text analysis, where the input text is
transcribed into a phonetic or some other linguistic
representation, and the second one is the generation of
speech waveforms, where the acoustic output is produced
from this phonetic and prosodic information. These two
phases are usually called as high- and low-level synthesis.
Abstract— The development of Machine Translation
system for ancient language such as Sanskrit language is
much more fascinating and challenging task. In the proposed
algorithm we integrate traditional dictionary rule based
approach for translation which translate source English
sentence into target Sanskrit sentence. It has two models Text
to Text Translator module and Text to speech synthesizer
module. Due to morphological richness of Sanskrit language,
this system uses morphological markings to identify Subject,
Object, Verb, Preposition, Adjective, Adverb sentences. This
paper presents English to Sanskrit approach for translating
well-structured English sentences into well-structured
Sanskrit sentences.
Keywords— Bilingual dictionary, Formant Synthesizer,
Natural language processing, Machine translation, Parser,
Rule based dictionary approach, Synthesizer, Translator.
I. INTRODUCTION
Machine Translation has been defined as the process that
utilizes computer software to translate text from one natural
language to another. This definition involves accounting
for the grammatical structure of each language and using
rules and grammars to transfer the grammatical structure of
the source language (SL) into the target language
(TL).Machine translation in Sanskrit is never an easy task
because of structural vastness of its grammar but the
grammar is well organized and least ambiguous compared
to other natural language.
The module present concerns with the Machine
Translation domain of Natural Language Processing. This
area of Artificial Intelligence is very useful in providing
people with a machine, which understands diverse
languages spoken by common people. It presents the user
of a computer system with an interface, with which he feels
more comfortable.
In the proposed methodology, for translation we decode
the meaning of the source input text in its entirety, the
translator must interpret and analyze the text, a process that
requires deep knowledge of the grammar, semantics,
syntax, idioms, etc., of the source language and target
language. The translator needs the same in-depth
knowledge to re-encode the meaning in the target language.
And for synthesizing we convert the generated text output
which is in Sanskrit language into the waveform and gives
the voice output.
II. APPROACHES USED
For translation generally there are three main approaches
used as follows,
1. Rule based,
2. Statistical based,
3. Example Based
Rule Based method for machine translation is divided in
to another three types,
1. Transfer based machine translation: - Type of machine
translation based on idea of Interlingua and is currently one
of the most widely used areas. It is necessary to have an
intermediate representation that captures the “meaning” of
the original sentence in order to generate the correct
translation.
2. Dictionary based machine translation: - Machine
translation can use dictionary based approach, which means
that the words will be translated as they are by a dictionary.
379
International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 2, Issue 12, December 2012)
3. Interlingual based machine translation: - Machine
translation can use dictionary based approach mean the text
to be translated, is transformed into an Interlingual, i.e.
source or target-language-independent representation. The
target text e is then generated out of the Interlingua.
Rule based translation consists of
1. Process of analyzing input sentence of a source language
syntactically and or semantically
2. Process of generating output sentence of a target
language based on internal structure each process is
controlled by the dictionary and the rules.
The strength of the rule based method is that the
information can be obtained through introspection and
analysis. The weakness of the rule based method is the
accuracy of entire process is the product of the accuracies
of each sub stage.
C. Pronoun
According to Paninian Grammar and investigations of
M. R. Kale, Sanskrit has 35 pronouns. These pronouns
have been classified into nine classes. Each of these
pronouns has different classes as personal, demonstrative,
relative, interrogative, reflexive, indefinitive, correlative,
reciprocal and possessive. Each of these pronouns has
different inflectional forms.
D. Adverb
Adverbs are either primitive or derived from noun,
pronouns or numerals.
TABLE I
COMPARATIVE STUDY OF ENGLISH AND SANSKRIT ON DIFFERENT
BASIS
Basis
Alphabet
Number of
Vowels
Number of
Consonants
Number
III. ENGLISH AND SANSKRIT GRAMMAR
English is well known and well structured language and
Sanskrit is ancient language and it is mother of all Indian
language. The English language has Subject+verb+object
format of sentence and Sanskrit is free ordered language.
This language does not lead to any ambiguity, thereby
maintaining a grammatical meaning for every sentence
obtained by the change in the ordering of the words in the
original sentence. The example has already explained in
section I.
Sentence
Order
Tense
A. Alphabet
The alphabet, in which Sanskrit is written, is called
Devnagari. The English language has twenty-six characters
in its alphabet while Sanskrit has forty-two character or
varanas in its alphabet. The English have five vowels (a, e,
i, o, u) and twenty one consonants while Sanskrit have nine
vowels or swaras (a, aa, i, ii, u, uu, re, ree and le) and thirty
three consonants or vyanjanas.
Mood
English
26 character
Five vowels
Sanskrit
42 character
Nine vowels
Twenty one
Thirty three
two: singular and
plural
SVO(subject-verbobject)
Three: present, past
and future
three: singular, dual
and plural
free order
Five: indicative,
imperative,
interrogative,
conditional
subjunctive
Six: present, aorist,
imperfect, perfect. 1st
future and 2nd future
Verb
Four: imperative,
potential, benedictive
and conditional
E. Verb
There are two kinds of verbs in Sanskrit: primitive and
derivative. There are six tenses (Kaalaa) and four moods
(Arthaa). The tenses are as present, aorist, imperfect,
perfect, first future, and second future. The moods are as
imperative, potential, benedictive and conditional. The ten
tenses and moods are technically called the ten Lakaras in
Sanskrit grammar.
A. Noun
According to Paninian grammar, declension or the
inflections of the nouns, substantive and adjectives are
derived using well defined principles and rules. The crude
form of a noun (any declinable word) not yet inflected is
technically called a pratipadikai.
F. Voice
There are three voices: the active voice, the passive
voice and the impersonal construction. Each verb in
Sanskrit, whether it is primitive or derivative, may be
conjugated in the ten tenses and moods. Transitive verbs
are conjugated in the active and passive voices and
intransitive verbs in the active and the impersonal form. In
each tense and mood, there are three numbers: singular,
dual and plural with three persons in each.
B. Gender
Any noun has three genders: masculine, feminine, and
neuter; In Sanskrit, there are three numbers: singular, dual,
and plural. And in English has two numbers: singular and
plural.
380
International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 2, Issue 12, December 2012)
D. Morphological Analysis process
The lexicon or the database developed plays a very
important role for Morphological analysis. As it searches
through the lexicon to gather the information. This process
takes the tokens as input and gathers grammatical
information on that token.
IV. OVERALL ARCHITECTURE OF ETSTS
The careful analysis of this module and its possible
solutions leads to the following design of the system. The
main idea behind dictionary based Machine Translation is
that input text sentence can be converted in to output
sentence by carrying out the simplest possible parse ,
replacing source word with the target language equivalents
as specified in a bilingual dictionary, and then using
grammar rules of target language re-arranging their order.
The overall system is diagrammatically shown below in
Fig.2, and following steps are involve in translation and
Synthesizer of System,
E. Translator process
This module performs the actual translation. The input to
this module is the parse. It also interacts with the
parser/generator module to get the parse of each word. It
then generates appropriate equivalents in English for the
morphological details of each word and ultimately presents
sentence in the correct order.
F. Parser /generator and mapping
This process checks whether the input sentence is
grammatically correct or not. The information gathered
from above mentioned process helps in analyzing the
grammatical aspect of the sentence and on the basis of the
rules the assessment is done. Mapping is done purely on
the basis of the information passed from the morphological
module. Parser Generator Module contains a set of
transducers built for individual Sanskrit words and
transforms strings to partial words, which are used by the
Vichheda module and dictionary based approach. It also
gives the parse of the words, which are used by the
sentence former to give the output in a structurally correct
sentence.
Figure 1 .Overall system design of Translator & Synthesizer
A. Input process
This process is the first small step towards translation
process. It takes sentence as input in a text box developed
in a GUI within the software developed for the translation
process.
G. Wave generation
After getting the target text it converts into the wave and
it plays using some output devices i.e speakers.
Synthesized speech can be created by concatenating pieces
of recorded speech that are stored in a database. Systems
differ in the size of the stored speech units; a system that
stores phones or diphones provides the largest output range,
but may lack clarity.
B. Token generation process
This module splits the given sentence into chunks of
strings delimited by spaces. These strings may be simple
words or compound words coalesced by the rule of English
Grammar. By applying the rules of English grammar assign
appropriate category to words like (noun, verb, noun phrase
etc.) Generate a parse tree using grammar rules of source
language.
V. ALGORITHM
The algorithm developed for the software is as follows,
A. Algorithm for Text to Text Translator Module
Step 1: Give the input as sentence in English.
Step 2: Split the sentence and generate the tokens from the
sentence based on white space.
Step 3: Store the tokens in the data structure like vector to
generate the loop.
Step 4: Generate the parse tree using grammar rules of
source sentence.
C. Vichheda Module process
The Vichheda module gets help from the set of
transducers to identify words and forms words through the
word generator. The word generator in turn takes the help
of the sandhi rules module wherever necessary. The
remaining string after the basic word is generated is sent
back to the Vichheda module.
381
International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 2, Issue 12, December 2012)
Step 5: After generation of basic word, the remaining
words sent back to Vichheda module.
Step 6: Analyze the on the basis of subject, verb and object.
Step7: Map the tokens with the grammar of target language
and generate the output in Sanskrit sentence.
4: Considering grammar rules of Sanskrit language
generate the Sanskrit sentence and after getting the proper
output sentence it forward to next synthesizer module.
Target sentence: Saha pustkam Pathati
He -> Saha (Subject)
Book -> Pustkam (Object)
Read -> Pathati (Verb)
B. Algorithm for Text to Speech Synthesizer Module
Step 1: The output from the first module i.e. translator acts
as the input to second module i.e. synthesizer.
Step 2: Analyzes the text and linguistics.
Step 3: Apply the phonetic alphabets to analyzed text.
Step 4: Generate the waveform and gets the output in
speech format.
<S>
VI. EXAMPLE
<V>
He
< noun >
< VP >
< VP >
<N>
pathati
pustakam
Figure 3 .Tree generated in target language
5: After getting the target sentence, by applying techniques
of synthesizing, convert sentence into waveform and gives
the output in voice format.
VII. RESULT
The objectives described for the ETSTS system above
may be implemented using different approaches. We use
here only one dictionary base approach. The separate
lexicons for English sentence and Sanskrit sentence may be
maintained in a database with morphological details stored
in the form of logics in the programming language used. A
bilingual dictionary will also have to be maintained in this
case. Rules are formed for tokenization and parsing.
Tokenization and parsing is implemented using java
language. Expected output screen after getting translation
means convert the English sentence to Sanskrit sentence is
given in Fig 4. Expected output screen after synthesizer
means convert the Sanskrit sentence into waveform and it
play using output devices i.e. speakers is given in Fig 5.
<N>
read
< noun >
Saha
<S
< VP >
< VP >
<V>
Consider the Example,
ES: - He read book
SS: - Saha pustkam pathati
1: Token generator separates each word from sentence
according to English grammar and it also consider the
space between two words.
He
read
book
Token1
Token2
Token3
2: Syntax tree represent the syntactic structure of a sentence
according to general grammar. In a tree, the interior nodes
are labeled by non-terminals of the grammar, while the leaf
nodes are labeled by terminals of the grammar. A program
that produces such type of ordered tree is called a parser.
3: After creating parse tree we get each word with proper
tagging then find the meaning of each word in English
dictionary and Sanskrit dictionary. If meaning of word is
not available i.e. dictionary its gives error massage.
< NP >
< NP >
book
Figure 2 .Tree generated in source language
382
International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 2, Issue 12, December 2012)
This work will very useful for the sharing the worldwide
knowledge with Indian. Traditional approach for translation
is just dictionary based which translate sentence by wordto-word comparison. In dictionary based approach of rule
base method of MT is possible it required dictionaries of
both the languages along with morphological databases of
both the languages. Speech synthesis has been developed
steadily over the last decades and it has been incorporated
into several new applications. For most applications, the
intelligibility and comprehensibility of synthetic speech
have reached the acceptable level. Formant TTS voices are
typically not as natural-sounding as concentrative TTS
voices and we use formant approach synthesizer for this
stage, but both provide capabilities that pre-recorded audio
cannot provide.
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Figure 4. Expected output screen after translator
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Figure 5 . Expected output screen after Synthesizer
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VIII. CONCLUSION
[9]
The translation of sentences from English to Sanskrit is
the main aim of the present paper. This may be
implemented using different approaches, we use here only
dictionary based approach To translate simple English texts
involving the need for Tokenize, applying grammar rule
create parse tree into corresponding appropriate sentences
in Sanskrit. Designing the Computer based translator is
possible using the natural language processing .Its followed
the rule-based approach for translation.
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