Machine Translation of Indian Signs for Endocrinologist Jagriti Mishra 1, GouriSankar Mishra 1,2, Kiran Ravulakollu 1,3, Ravi Rastogi 1,4, K.M Rafi 2,5 1 Department of Computer Science & Engineering, School of Engineering & Technology, ShardaUniversity.Greater Noida, INDIA 2 Key Foundation, New Delhi Abstract India being the second most populated country in the world with over a billion population and over a million hearing impaired and diabetes disease patients, a translation system which can translate a given input into sign languages can be used to disseminate information to the million hearing impaired patients. Such people find it difficult to access information in common places like hospitals and railway stations. A translation system which can convert English into Indian Sign Languages can be developed to help such people. Machine Translation (MT) is an innovative paradigm that promotes the advancement of science and technology to built smart environments. It advocates an invisible technological support layer of information processing to improve the quality of life. In this paper we discuss. Present study contains possibilities of integrating concept related to natural language processing (NLP) certain aspects of integrating English languages to sign language. Keywords MT, Sign, NLP, ISL, Linguistics I. INTRODUCTION Natural Language Processing (NLP) is the ability of computers of computer programs to understand the human language. Machine Translation is a field of Natural Language Processing which deals with automated translation. It is the process by which computer software is used to translate a text from one natural language (such as English) to another (such as Spanish or Sign language). Sign Linguistics, on the other hand, is a relatively new field of study in the area of Linguistics, and the combination - machine translation of sign languages - is less than twenty years old [15].Sign languages as languages have not been studied as extensively as spoken languages, and there is still much to be learned about them. Sign Linguistics, on the other hand, is a relatively new field of study in the area of Linguistics, and the combination machine translation of sign languages - is less than twenty years old [15]. With the requisite of a translation system able of converting an input into Sign Language and accompaniment of adequate advantages of developing such a system, Scientists are now trying to develop translators for specific domains to assist hearing impaired people. II. RELATIONAL WORK Computer is an integral part of human life now-a-days. In day-to-day life human depends upon computers for their various needs, right from laundry to dissection of human body, computers are involved in everything. Scientists and engineers are now trying to make computers understand natural languages. III. PAPER ORGANIZATION In section A we have introduced Natural Language, Machine Language, ISL [1] and in section B we have mentioned Includes Problem Foundation, Methodology. Section A 1. Natural Language Processing (NLP) is the computerized approach foranalysing text that is based on both a set of theories and computational techniques for analysing and representing naturally occurring texts achieving human technologies. Natural LanguageProcessing is a theoretically motivated range of - like language processing for a range of tasks or applications. The goal of NLP is to accomplish human-like language processing. 2. Machine Translation The objective of Machine Translation is to restore the meaning of original text into translated verse. In general the translation process has two levels: Metaphrase Metaphrase means word to word translation. The translated version will have literal translation for each word in text. But, translated text may not necessarily convey meaning of original text, i.e. the semantics of original text may be lost. Paraphrase The translated text will contain the core of the original text but may not necessarily contain the word to-word translation a) Machine Translation Architecture Machine translation system can be loosely grouped into three basic designs: Direct, Transfer, or Interlingua shown in Fig 1.2. 112
Direct systems base their processing on the individual words of the source language string. In direct systems translation is obtained without performing any kind of syntactic analysis on the source input. In transfer systems, the input text is analyzed for syntax to some extent and then a special set of transfer rules are employed to read the information of the source language structure and produce a corresponding syntactic or semantic structure in the target language. In Interlingua systems, the text is analyzed and transformed into intermediary language which is independent of any of the languages involved in translation. The translated verse for the target language is then derived through this intermediate representation. [3] These figures are extrapolated from the number of people who are deaf and hard hearing in western nations. It would be realistic to believe that the actual number of people who are deaf and hard of hearing must be much higher, Increasing awareness about the nature of sign languages is evidenced in statements such as: Through sign languages, there is free and easy communication, Sign language is the mother tongue of the Deaf, and the like [12]. Scientists are trying to develop systems which can translate a given set of input text into an equivalent desired source language. One such system is developed by Purushottamkar et al [2]. They reported a cross-modal translation system from Hindi strings to Indian Sign Language (ISL) [11]. Though the number of deaf people learning ISL is rising there are only few people in the society who can converse with them in sign languages. Due to the paucity of people who can converse in ISL, the pain and trouble of the deaf and mute community still prevails[10]. A lot of problem is faced by the deaf and mute people during their visits to the doctors. Section B Fig.1.2 Machine Translation Architecture A translation machine can be developed to convert English text into Indian Sign Language (ISL)[4][5]and such a natural language processing system can lessen the pain and difficulties of deaf and mute citizens of India as shown in Fig 1.2(a). 3. Indian Sign Languages Fig. 1.2(a)Sign Representations India estimated that there are over a million people who are profoundly deaf and approximately 10 million hard of hearing people, in India. 1. Problem Description A translation system converting English text into Indian Sign Languages can be developed [9]. Developing a system dealing with all the possible domains in medical sciences is a tedious work. The development can be done domain wise. A system converting English text into Sign Languages for possible use by Endocrinologist, i.e. the doctor who specializes in dealing with Thyroid patients, can be developed. [6][7]The doctor will type his text that he wants to convey to the patient and the input text will be converted into an equivalent sign language output.[13][14] The deaf can interact with the doctor by writing his problems or else a system using pattern recognition can be developed to translate the pictorial input into comprehensive sequence of text or speech. 2. Methodology The translation system can be developed using python environment [6] which is usually the preferred programming platform for developing expert systems involving a lot of user inputs as shown in Fig.2. Direct or transfer architecture can be used to develop the required translation system. The following approach can be adopted to achieve the goal:- 113
b) Phase II Phase II involves development of the translation system. The input text will be parsed for its syntax which will then be tokenised. Tokenisation refers to breaking of the input text into small units called tokens. These small units can be words. After being broken into smaller units, a parsed tree will be established according to the grammar of English, i.e. Subject- Verb- Object. Then the English parse tree will be translated according to the grammar of Indian Sign Languages, i.e. Subject-Object- Verb. Then the translated text can be achieved. Fig 2 Methodology a) Phase 1 First phase of work includes collection of data for developing the translation system. For the development of the proposed work, all the possible conversations among diabetic patients and doctor should be available. I have collected all the possible conversations between an Endocrinologist and a patient from various hospitals and doctors. Conversation between a doctor and patient includes the questions that a doctor asks, and the suggestions doctor gives to his patient[8]. A sample of questions and answers has been mentioned below:- Questions - 1. Do you often feel hungry? 2. Are you eating enough but still losing weight? 3. Are you gaining weight? 4. Do you often suffer from fatigue? Suggestions- 1. Avoid mango 2. Avoid beet roots 3. Avoid potato 4. Consume fenugreek powder with milk or water 5. Reduce salt intake 6. Reduce sugar intake The next target is to understand the grammar of Indian Sign Languages, which has also been achieved. c) Design The system will take English text as input. The doctor can type the question or suggestion in a provided box. The box can show suggestions while the doctor types his input. The output shown to the patient can be in the form of pictures projected on a TV screen or a Computer screen. The images can be projected through animation or through captured photographs. Front end which will take text as input may look like the following figure Fig.3(a) and Fig.3(b):- Fig. 3(a) Translation System for Endocrinologist 114
The patient can write his problems to the doctor or a system can be developed which can capture images and can convert them into English text. The proposed system will convert the text typed by the doctor into Indian Sign Language understandable the hearing impaired patient. d) Challenges Fig. 3(b) Translation System for Endocrinologist Collection of all the possible patient- endocrinologist conversation is a daunting task. A lot of doctors have to be consulted in order to make sure that all the data has been collected. Understanding grammar of ISL- The amount of work done in India to develop Indian Sign Language is really inadequate. It is difficult to find research papers and books for Indian Sign Language. Many English words do not have an assigned sign The words which do not have an assigned signed in Indian Sign Language have to be spelled. Differences in dialects India are a country where language changes after every mile. The differences in dialects can be observed in Indian Sin Languages also. IV. CONCLUSION In this work a translation System is being developed which will convert English text into Indian Sign Languages for possible use by Endocrinologists. Hearing impaired people feel a lot of trouble when they have to access information in common places like hospitals and railway stations. Only few people can know how to express their thoughts using sing languages. Such a translation system can be an aid for hearing impaired people in public places. Diabetes is a prevailing disease in India. Many diabetic patients must be hearing impaired. Diabetic patients need to visit their doctors on a regular basis. V. FUTURE SCOPE The proposed system deals with a specific domain. It will only translate sentences used by endocrinologists. The system can be extended to deal with various domains of medical and health sciences. The system can include domains like cardiology, neurology, nephrology, etc. REFERENCES [1] Dasgupta, T., &Basu, A. (2008). An English to Indian Sign Language Machine Translation System www.cse.iitd.ac.in/embedded/ assistech/proceedings/p17.pdf [2] Kar, P., Reddy, M., Mukherjee, A., and Raina A. M. (2007) INGIT: Limited Domain Formulaic Translation from Hindi Strings to Indian Sign Language International Conference on Natural Language Processing (ICON), Hyderabad. [3] Mathew P. Huenerfauth (2003) A Surveyand Critique of American Sign Language Natural Language Generation and Machine Translation Systems, University Of Pennysylavania. [4] Shangeetha, R.K.; Valliammai, V.; Padmavathi, S., (2012) "Computer vision based approach for Indian Sign Language character recognition," Machine Vision and Image Processing (MVIP), 2012 International Conference on, vol., no., pp.181,184, 14-15. [5] Sinha, S. (2007) A skeletal grammar of Indian sign language. PhD thesis [6] Stokoe, W. C. (1960) Sign language structure: an outline of the visual communication systems of the American deaf Silver Spring, MD: Linstok Press. [7] Tariq, M.; Iqbal, A.; Zahid, A.; Iqbal, Z.; Akhtar, J. (2012) "Sign language localization: Learning to eliminate language dialects," Multitopic Conference (INMIC), 2012 15th International, vol., no., pp.17,22, 13-15 Dec. 2012 [8] Tmar, Zouhour; Othman, Achraf; Jemni, Mohamed, (2013) "A rulebased approach for building an artificial English-ASL corpus," Electrical Engineering and Software Applications (ICEESA), InternationalConference on, vol., no., pp.1, 4, 21-23. [9] Ulrike Zeshan, Madan M. Vasishta, MeherSethna (2005) Implementation of Indian Sign LanguageI in Educational Settings, Asia Pacific Disability Rehabilitation Journal, Vol 16. [10] Banerji JN. India. International Reports of Schools for the Deaf, 18-19.Washington City: Volta Bureau, 1928. [11] Cross J. Toward a standardized sign language for India. Gallaudet Today, 1977; 8(1): 26-29. [12] Zeshan U. Gebärdensprachen des indischensubkontinents. [Sign languages of the Indian Subcontinent] Munich: Lincom Europa, 2000. 115
[13] Alison Wray, Stephen Cox, Mike Lincoln and Judy Tryggvason, (2004) A formulaic approach to translation at the post office: reading the signs", Language and Communication, 24: 59-75. [14] UlkrineZeshan Sign Language in Indo-Pakistan- A Description of Signed Languages, John Benjamins B.V., 2000 [15] Morrissey, S., and Way, A. An example-based approach to translating signlanguage. 116