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" " " "ےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےےے)D " " " " " " " " "Xh: Journal of Advanced Research in Information Technology, Systems & Management Vol 3 Issue1 Date of submission: Dec 27 2019 Date of acceptance: 3 Jan 2020 Page 14-20 Dictionary based English Speech to Marathi Speech Translation Corresponding author: 1st Author Ruchita S. Jadhav. Matoshri College Of Engineering And Research Centre,Nashik HYPERLINK "mailto:ruchitajadhav73@gmail.com" ruchitajadhav73@gmail.com 3rd Author Pratiksha N.Shirude. Matoshri College Of Engineering And Research Centre,Nashik HYPERLINK "mailto:pratikshashirude49@gmail.com" pratikshashirude49@gmail.com 2nd Author Monali P. Tile. Matoshri College Of Engineering And Research Centre,Nashik HYPERLINK "mailto:monalitile3003@gmail.com" monalitile3003@gmail.com 4th Author Pallavi D. Wackchaure. Matoshri College Of Engineering And Research Centre,Nashik HYPERLINK "mailto:Pallaviwakchaure98@gmail.com" Pallaviwakchaure98@gmail.com ABSTRACT People of different language producing background could not able to interact with each other. This concept of translation will help people to communicate each other with well off. Also it will help to fill communication gap between two linguistically different backgrounds. It will help to the people who belongs from different villages, who have taken education of English. Majority of the Indian community is not recognized with English while most of the information available on web or electronic information is in English. Many Time government documents and forms are been presented in English Language where a lay man from Marathi language background finds difficulty to understand information and even avoid. Machine Translation able to translate Information presented in one language to other language with proper meaning. Information can be present in form of text, speech and image translating this information helps for sharing of information and ultimately information gain. The main objective of Machine Translation is to removed the language barrier in a bilingual nation like India. Machine Translation gives several approaches to translate source language to target language. In this project we are translating English to Marathi sentences for kids based on dictionary.. Keywords Natural Language Processing, Rule-based Machine Translation, Machine Translation,speech recognition, ,Translation,Morphology,Translator,Tokenization,POS Tagging. INTRODUCTION In India, English as a language has played a major role in administration, legal and education sector since British period.Hindi and Marathi is a widely spoken language and it is the simple to talk. On the other hand, English is worldwide popular language. Presently, an awareness has been developed in this country for using regional languages such as Marathi , Kannada etc. for government sector, education and every other domain of general life. In this circumstances, it has become very crucial to set up system which can translate English to various languages. With the existence of huge text resources in internet and India being one of the most well known users of web, even trading companies are finding it necessary for building machine translation. There are three types of machine translation framework : Rule-Based Machine Translation (RBMT):- This schemes use large collections of rules which is physically advanced over time by human experts mapping format from the source language to the target language. In rule-based systems aids allocate good automated translations with expected results. Rule-based schemes is diminutive bit costly and time consuming to implement as well as to preserve. Statistical Machine Translation systems (SMT):- The System is use computer algorithms to produce a translation . The primary ideas of arithmetical machine translation were familiarized by Warren Weaver in 1949. Statistical models consist of words and phrases learned automatically from bilingual parallel sentences, creating a bilingual “database” of translations. Hybrid Machine Translation(HMT):-Hybrid-based method is developed by taking the benefit of both statistical and rule-based translation methodologies which has proven to have better efficiency in the area of MT systems. We can used hybrid method in a different way. In some cases, translations are performed in the primary stage using a rule-based method succeed by complementary or exactly the result using statistical information. In the other way, rules are used to pre-process the input data as well as postprocess the statistical output of a statistical-based translation system. This method is better than the before two methods. English to Marathi language Translator (EMLT) is a arena of Machine translation worried with the interactions between computers and human (natural) language. EMLT systems change info from computer databases into legible human language. Natural systems convert samples of human language into more formal representations such as parse trees or first-order logic structures that are easier for computer programs to manipulate. Rule Based English to Marathi Translator converting Simple English affirmative sentences to Marathi. In this project we are converting the simple English affirmative sentences to Marathi sentences for kids. This is basically a machine translation. We have chosen the transfer-based approach which is the thin line between the semantic and the direct approach. For that we have designed the parser which helps us to map the English sentence binding to the rules and then getting converted into target language Machine Translation is translation obtained by machine on wide-ranging from source to target language. English is the extensively used language all ended world. World has acquired the English as considerable communication language. Marathi terms are derived from Sanskrit ‘Nava’ derived from ‘Navin’, month in English ‘Maas’ resulting from ‘Machine’. Persons from different culture and language base are not able to easy communicate where a translation system would facilitate to complete the gap. Marathi is mother tongue language of Maharashtra with world of information many articles and web articles are been written in English languages mostly in typical(regional) language. Abundant amount of information is written by language specialist on specific topic related to regional spoken language .problem lies to understand this doc’s and articles in different language for which computer assisted translation is faster and better solution than human assisted translation. Government and Educational sector needs a Translation system for assistance and communication orders Large research effort is been taken by major organizations like IIT Bombay ,C-DAC ,IIT Hyderabad for better fully automated machine translation system. Literature Survey Once we get the idea about problem occurred then we have to analyze that problem. For this analysis we have to study total concept behind the problem. Before going to make new system it is more important to study the existing system. From this study we get to know what kind of requirements are fulfill till date and how to make the system more advanced and efficient than previous one using the latest technology. Our proposed system involves following research paper analysis: According to Prof.Priyanka Kulkarni,Prof. Abhay Adapanawar we can build a system which can be used for kids to get Dictionary Based English to Marathi Speech Translation. For that they use ISSN 2229-5518 standard. In this system they are dealing with the rule-based English to Marathi translation of assertive sentences. This is basically a machine translation. In this system they are successful finished numerous processes such as tokenization, part of speech tagging etc. Database of production rules is preserved which plays important role in translation. English to Marathi bilingual dictionary has been designed for the purpose of language translation. In this System there are separate sentence patterns for English and Marathi sentences. These rules are in pair wise. Since a sentence pattern in English must have a consistent sentence pattern in Marathi which is used for language translation. These rules are predefined and must be precisely assumed in the language translation system. For the language translation determination, an English sentence pattern will change to a Marathi sentence pattern according to a specific rule. This rule is given in the production rule table. In this table there are very few rules represented to give the idea that how the production rule works. The database consists of a sequence of lexical categories for Marathi language which are mapped to its corresponding English language sequence, which is to be used in the target grammar generator. When an exact set is queried by the Target Language Generator the rules database revenues a precise sequence to be used for accurate translation after rearrangement of words. The lexicon has been physically built for around 4000 words in English. The lexicon is categorized just like a dictionary in the xml format. It contains of dictionary entries as English words and their corresponding Marathi words. The words unchanging have their morphology i.e. morphological as well as semantic properties to define that word. In Existing system there is no database is ready for the grammatical rules of the source language and target language. The database contains of a order of lexical groups for Marathi language which are planned to its conforming English language sequence, which is to be rummage-sale in the target grammar generator. When a detailed set is inquired by the Target Language Generator the rules database revenues a precise arrangement to be used for precise translation after reorganization of words. In planned System Target language producer is practical using three components: Word to Word Translator, Re-arrangement Algorithm and Target Language Sentence Generator. The Word to Word Translator translates the source language words into target language words by means of the Bilingual Lexicon. Re-arrangement Algorithm then re-arranges these target language arguments into the correct target language sentence structure. The Target Language Generator receipts this output and displays the sentence into the target language. The Planned scheme is valuable for instructive drive for kids to English into Marathi language translation for simple sentence. PROBLEM STATEMENT The English to Marathi translation is already used in various system, but in existing system English to Marathi translation is limited for translation of specific domain.In proposed system this drawback is removed.This drawback is removed by an Dictionary Based English to Marathi Speech Translation. In this system the terminology used as source language is English and Target language is Marathi. Every language has parts of speech i.e. Verb, noun preposition, etc. Structure of language changes depending on the arrangement of parts of speech. For e.g.-“I am going to school”. This is one English sentence. Here “I” is a subject; “am going” is verb phrase. Verb phrase means “auxiliary verb+ subsequent verb” and “to school “is an object. So structure of sentence is “Subject+Verb+Object”.Translation of this sentence in Marathi is “Mi shalet jaat ahe”.’I’ is translated as ‘Mi’in Marathi, ‘am’ becomes ‘ahe’,’going’ becomes ‘jaat’ and ’to school’ becomes ‘shalet’in Marathi. Here “Mi” is Subject, ”shalet” is an object and “jaat ahe” is a verb. So structure of sentence in Marathi is “Subject+Object+Verb”. For proper language translation, it is necessary to understand the grammar of both languages. Need of Translation English to Marathi translation is huge topic and many people have already work on that topic,but not yet on speech to speech translation with more accuracy. Students of different linguistic background can not be able to interact with each other, who belongs to Convent school and western culture. This concept of English to Marathi translation will help people to communicate comfortably. Also it will help to fill communication gap between two linguistically different backgrounds. System Block Diagram Fig 1: Block Diagram 5.1 Speech recognition Speech recognition is the aptitude of machine or package to classify words and phrases in spoken language and change them into machine decipherable format. Speech recognition is the distinguish spoken words which can be rehabilitated to text, classify the creature based on their voice. Explain the analog waves of voice into digital data by sampling the sound. Speech translation is the process by which conversational spoken phrases are promptly interpreted in second language. 5.2 Speech to text conversion Speech to text converter tool is used to change any voice into plain text. Instead of typing you can just speak and this tool can convert it into text. A real time speech to text adaptation system convert the spoken words into text from exactly in the alike way that user articulates. We are generating a real time speech recognition system that is tested in real time noiseous atmosphere. Real time dialog to text adaptation system presents conversion of the uttered words promptly after utterance. The determination is to present a new speech recognition system that is computationally unpretentious and additional robust to noise than HMM based Speech recognition system. Work on Desktop,Laptop,Mobile phones. Use high quality microphone for best performance. 5.3 WEB API for translation WEB translation API can dynamically translate the text between thousands of language pairs. We used readymade Web API (Application Program Interface) used for translation. Web API is a framework that makes it easy to build language translation services. 5.4 Mapping Of Audio File: Audio mapping is a mapping technique. It is a way of recording data which is stored in database. There is a huge range of variety in audio mapping technique. For English into Marathi language translation there could be many possible spellings for one sound. You can spell out but it can be hard to notice the ambiguity to remember to do this when recording and it is easy to miss appostraphs etc. 5.5 Play Audio File: The file which is mapped into audio mapping will play appropriate audio clip considered as a output. 5.6 Speech to Text: After mapping the audio file will also be converted into Text format. Thus, the output will be displayed in both Speech as well as Text Format. REFERENCES [1] R. K. Ando and T. Zhang. A framework for learning predictive structures from multiple tasks and unlabeled data. Journal of Machine Learning Research (JMLR), 6:1817–1953, 2005. [2] R. M. Bell, Y. Koren, and C. Volinsky. The BellKor solution to the Netflix Prize. Technical report, AT&T Labs, 2007. http://www.research.att.com/کvolinsky/netflix. [3] J. Sangeetha et al, An Efficient Machine Translation System for English To Indian Languages Using Hybrid Mechanism, International Journal of Engineering and Technology (IJET) ISSNJ. [4] Wren P. and Martin H. High School English Grammar and Composition. S Chand Publication [5] Technology Development for Indian Languages, DIT, Government of India Also available at: http://www.tdil-p http://www.saakava.com [6] Jenny Rose Finkel, Trond Grenager, and Christopher Manning. 2005. Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling. In proceedings of the 43nd Annual Meeting of the Association for Computational Linguistics (ACL 2005), pp. 363-370. [7] Esha Palta. 2006-07. Word Sense Disambiguation. Master of Technology First Stage Report, IIT Bombay. [8] Walker D. and Amsler R. 1986. The Use of Machine Readable Dictionaries in Sublanguage Analysis. In Analyzing Language in Restricted Domains, Grishman and Kittredge (eds), LEA Press, pp. [9] Spector, A. Z. 1989. Achieving application requirements. In Distributed Systems, S. Mullender [10] Bowman, M., Debray, S. K., and Peterson, L. L. 1993. Reasoning about naming systems. . 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