Role of Language Engineering to Preserve Endangered Language
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Language engineering can help preserve endangered languages by developing tools like language translators, speech generation systems, and language teaching systems. If these tools are applied to endangered languages, they may help prevent the languages from going extinct by increasing the number of people who understand and use them. Key applications of language engineering that could support endangered languages include speech synthesis, machine translation, speech recognition, text-to-speech conversion, and language teaching systems. Developing digital language documentation and creating transcription tools can also help endangered languages survive by making more materials available to study and learn them.
Role of Language Engineering to Preserve Endangered Language
- 1. Role of Language Engineering to Preserve Endangered Languages Amit Kumar Jha Ph.D. (Informatics and Language Engineering) School of Language, MGAHV, Wardha Sumit Kumar Gupta MILE, School of Language, MGAHV, Wardha National Conference on the Approaches & the Methodologies on the Study of Indegnous & Endangered Language Dr. Piyush Pratap Singh Asst. Professor School of Language MGAHV, Wardha
- 2. Endangered Language • Endangered language (EL) is the language community incorporates less number of speakers of that particular language. • EL is likely to become extinct in the near future. Many languages are failing out of use and being substituted by others is more widely used in the region or nation.
- 3. Language Engineering • Language Engineering (LE) is the subfield of computer science which explores the field of language related software and its feasible hardware development.
- 5. Goal of Language Engineering • The ultimate goal of LE is to develop a machine which is able to understand and generate natural language. • If the Approaches of LE implements on EL, then EL may be Preserve.
- 6. Language Endangered • The loss of speakers in one language is the gain of speakers of another language, except for cases of genocide. Languages are generally replaced when an entire speech community shifts to another language. Replacing languages are very often official state languages. • The world is experiencing an unprecedented wave of language extinctions. There are between 6,000 and 7,000 languages currently spoken, and between 50 to 90 per cent of those will be extinct by the year 2100.
- 7. Language Extinction Results • Language extinction results in loss of cultural identities, knowledge systems, and the variety of data needed to understand the structure of language in the mind. • Documenting endangered languages preserves data and stimulates language maintenance and revitalisation.
- 8. Language Documentation • Many of these languages do not have a written tradition and written data may be completely unavailable or sparse, the languages are not used in the media, or their speakers do not use the Internet (and if they do, they often use another language). In such cases, linguists must start from scratch and collect as much data as possible by recording speakers of a given language. • Ideally, language documentation contains representative samples from different speakers – representing different age groups, different professions, of both sexes, and different origins –, but in the case of endangered languages this may not be possible, because the number of speakers is too small and/or there are only elder speakers. An important issue apart from the number of speakers and amount of data concerns the communication between the linguists or other researchers who want to document a language, and the language community.
- 9. Language Documentation • In the case of endangered or minority languages, the documenters often are outsiders, not members of the community. They may not be fluent speakers of the language in question and can communicate with the speakers in a second or a third language. This often leads to an unnatural use of the language that is to be documented.
- 10. Digitalization • Digitlizaion is the process in which data is the store in the form of digital. The durability of digital data is more than others types of data. To preserve EL by Digitaliztion we convert and store data in digital forrm i.e. text, sound, image etc. The researchers should create study meterial of EL in digital form.
- 11. Application of Language Engineering • Speech Generation • Language Translator • Speech-to-Text • Text-to-Speech • Langauge Teaching • Translitration Tool
- 12. Application of Language Engineering... • Speaker Identification • Verification Speech Recognition • Character and Document Image Recognition • Question-Answering System • Word sense Disambiguation • Information retrieval and Information Extraction • Film Production and Dialogue Debbing
- 13. Speech Generation • With the help of language engineering we can generate the speech of Endangered Language by a machine. If a machie will be able to generate EL then we can preserve that Language.
- 14. Language Translator • Language translator or Machine translator is a machine which is able to translate one language to another language. The first language is called source language and the second language is called the target language. If the Source language or the target language is EL, EL is preventing by this Language Translator system.
- 15. Speech-to-Text • It is the process of converting speech to text. This is the task of documentation. If we convert speech file to text file of EL then we preserve that language.
- 16. Language Translator • Language translator or Machine translator is a machine which is able to translate one language to another language. The first language is called sourse language and the second language is called the target language. If the Sourse language or the target language is EL, EL is prevent by this Language Translator system.
- 17. Transcription Tool • Transcription is the process in which one script to another script. • A person which is unknown to a specific language, its script and pronunciation, the role of Transcription tool is importnat in this context. • If Transcription tool for an EL will be developed then we increase the number of people to understand that language.
- 18. Text-to-Speech • Text-to-speech system is the system in which text data is input and it return speech data as output. It plays important role in Man-Machine interaction.
- 19. Langauge Teaching • Language Teaching is the process of teaching a language. With the help of LE we can create a system for teaching a language. If EL teaching system is created EL may be preseve. As it is known that there are some language which has the speakers of old age and this language doesn’t transfer to the next generation. After some that language becomes dead. To preserve this language this system is important.
- 20. Question Answering System • Question-Answering system is a Natural Language Processing system. If a person ask a question to the system, system returns the answer of that question.
- 21. Extinct Language • An endangered language is a language that is at a risk of falling out of use, generally because it has few surviving speakers. If it loses all of its native speakers, it becomes an extinct language.
- 22. Levels of Endangerement • UNESCO defines four levels of language endangerment between "safe" (not endangered) and "extinct": 1. Vulnerable 2. Definitely endangered 3. Severely endangered 4. Critically endangered
- 23. EL in India • Indian Goverment started a scheme to preseve EL the name of this Scheme is SPPEL(Scheme for Protection and Preservation of Endangered Languages). • The SPPEL has listed 117 languages to be documented in its current phase. The Languages are some of lesser known Indian languages which are spoken by less than 10,000 speakers.
- 24. Refrence • Refrence List : • B. WEBBER, M. EGG and V. KORDONI (2012). Discourse structure and language technology. Natural Language Engineering • Jurafsky, Martin (et.al. ) Sppech and Language Processing. Prentice Hall, Englewood Cliffs, New Jersey 07632 • Reiter, E. and Dale, R. (2000). Building Natural Language Generation Systems. Cambridge University Press, Cambridge. • Yarowsky, D. (1996). Homograph disambiguation in text-to-speech synthesis. In Progress in Speech Synthesis, pp. 159–175. Springer-Verlag, Berlin. • Small, S. L. and Rieger, C. (1982). Parsing and comprehending withWord Experts. In Lehnert,W. G. and Ringle, M. H. (Eds.), Strategies for Natural Language Processing, pp. 89–147. Lawrence Erlbaum, New Jersey. • www.sppel.org