By: Chitwan Gupta 1 & Aditi Sharma 2
1,2 Department of CSE, Chandigarh College of Engineering and Technology, Chandigarh, India
e-mail: 1 co22320@ccet.ac.in, 2 lco22393@ccet.ac.in
Abstract
A language is one of the ways for preserving customs, history and heritage. When a language is preserved, it allows its culture to remain alive. One surprising way that AI is being used to revive endangered and indigenous languages is through machine learning algorithms that can transcribe and translate oral histories and stories from endangered language speakers. This technology allows for the preservation of valuable cultural knowledge that might otherwise be lost forever. It also helps in Speech Recognition, Text- Analysis, Images and Audio Processing. Furthermore, by providing Interactive Language Learning Platforms, Speech Recognition and Pronunciation Practice, Language Acquisition Support, AI becomes a foundation in safeguarding and revitalizing endangered linguistic heritage.
Keywords
Artificial Intelligence, Language Preservation, Language Revitalization, Machine Translation, Transcription, Adaptive Learning, Speech Recognition, Linguistic Diversity, Neural Networks
Introduction
The revival and recovery of endangered as well as extinct languages has become essential to maintaining diverse culture and identity in an increasingly globalized world. Although human resources have historically been the mainstay of attempts to conserve those languages, artificial intelligence (AI) [1-3] is becoming more and more important in same. The work done to record, protect, and preserve endangered languages that are in danger of going extinct is referred to as language maintenance. On the other side, language renewal attempts for bringing back to life lost languages by rebuilding and reintroducing them into society.
By helping to document endangered languages, artificial intelligence (AI) along with machine learning [4–7] can play a crucial role in language restoration. AI technology can be used by linguists and academics to efficiently record linguistic statistics, such as vocabulary, grammar, and pronunciation. This can assist in preserving important knowledge about those languages for the next generations. Synthetic intelligence can also aid with transcription and language translation. Translation and transcription services can become unique and effective when machine learning algorithms are trained to identify speech and writing styles. This is particularly useful for endangered languages, which could also lack extensive translation resources, in terms of understanding and retention. AI’s application in learning and teaching is another way it may help with language protection. Artificial intelligence (AI)-driven language learning platforms can provide personalized, dynamic study evaluations, reducing the barrier to learning endangered languages. These platforms can adjust instructions based on the learner’s preferred learning method, aptitude level, and natural language processing abilities using speech recognition and adaptive mastery approaches.
Artificial intelligence has the ability to renew and preserve languages. Artificial intelligence systems that analyze linguistic material, such as written data, historical files, and related languages, can be used to revive extinct languages. This can help to recover languages that have been lost for generations, allowing individuals to reconnect linguistically. AI can also help with the design of study materials for revitalized languages. By merging modern language sources with cultural artifacts, artificial intelligence may be able to provide grammatical rules, vocabulary, and even audio recordings for revived languages. This can assist language learners learn and practice their languages in everyday speech. Although the promise of AI to preserve and revitalize languages is intriguing, there are significant challenges to overcome.
These problems include the requirement for detailed linguistic statistics, ethical concerns about AI-generated material, and competence biases in language processing systems. However, recent trials and employment studies indicate that AI has the potential to make a significant contribution to language protection and revitalization. As AI technology progresses, the prospects for language preservation and re-suscitation appear to be vast. Emerging AI technology, such as neural gadget translation and speech recognition, can help to improve language documentation and translation processes. However, it is vital to address ethical concerns and employ AI responsibly and jointly with language groups. Finally, artificial intelligence has the potential to change language protection and restoration actions. Artificial intelligence can help to preserve linguistic diversity and cultural legacy by recording extinct and endangered languages. Despite these limitations, AI has a promising future in the field of language preservation and revitalization.
Understanding the Preservation and Revival of a Language
Protecting and also reviving extinct as well as endangered languages is the goal of language renovation and renewal, two related processes. Comprehending the notions and significance of language revitalization and restoration is crucial for appreciating the possible involvement of AI in those undertakings.
Importance of preservation of language
Language preservation stands as a critical work in cultural diversity. Languages are more than just communication tools; they include vast libraries of distinct knowledge systems, historical views, and cultural practices. Preserving a language becomes synonymous with safeguarding the very core of a community’s identity and heritage. It ensures that the wealth of cultural nuances and wisdom encoded in languages perseveres, contributing to the collective legacy of humanity for generations to come.
When a language is deemed endangered, it faces the imminent threat of vanishing entirely from the human experience. Globalization, urbanization, and linguistic assimilation act as formidable forces eroding linguistic diversity, often pushing languages to the brink of extinction. In the face of such challenges, language preservation projects emerge as beacons of resilience, seeking to document, protect, and breathe new life into endangered languages. These projects play a crucial role in stemming the tide of linguistic erosion and preventing the irreparable loss of cultural richness.
When endangered languages decline to the point of extinction, distinctive ways of expressing ideas, identities, and cultural legacies are lost. Thus, language preservation initiatives work as preventative measures to defy this trend. They actively seek to maintain and revitalize endangered languages, going beyond simple documentation. By means of extensive endeavors that encompass linguistic research, community participation, and educational activities, these projects establish a strong basis that enables languages to not only survive but also flourish in the face of modernization’s obstacles. Language preservation initiatives, by their very nature, act as defenders of cultural legacies, pushing off an increasing threat of homogenization. They represent a dedication to diversity and recognize the intrinsic worth of every language.
Revival is worthy of attention– Language revival aims to revive extinct languages. When a language becomes extinct, it means that no native speakers remain and it is no longer utilized for communication. However, with hard work, it is feasible to revive these languages by recreating and reintroducing them into communities.
Language revival has significant cultural and social importance. Reviving a language not only reconnects communities with their linguistic roots, but it also renews the related cultural practices, traditions, and ways of thinking. It promotes a sense of identification and belonging in the community, as well as pride and empowerment.
.
Cooperation between preservation and revival- The close connection between language revitalization and preservation is essential to the bigger goal of preserving the diversity of languages and cultural legacy. These two projects are closely related, with preservation providing the framework for revival and revival providing the drive and inspiration for preservation efforts.
The underlying pillar for any subsequent language revival efforts is the preservation of endangered languages, which calls for precise documentation and the preservation of linguistic components including grammar, vocabulary, and oral traditions. In addition to guaranteeing the preservation of a language’s basic constituents, this archival work offers a wealth of resources that will be crucial for upcoming language revival initiatives. On the other hand, preservation incentives are significantly impacted by the effectiveness of language revival attempts.
Collaboration across disciplines is a dynamic force in both preservation and revival initiatives. Linguists contribute their specialized expertise in documenting and preserving linguistic elements, community members bring cultural insights and invaluable oral traditions, while AI researchers play a crucial role by employing innovative methodologies and technologies. This collaborative effort forms a synergistic alliance, blending academic rigor with community knowledge and technological advancements.
Additionally, language recovery programs highlight the interplay of technology innovation and linguistic competence. By bringing cutting-edge technological advancements, AI researchers make a substantial contribution in collaboration with linguistic experts. The creation of advanced speech recognition software, language learning applications, and other AI-driven tools are some examples of these developments. By bridging the gap between tradition and modernity, these technologies not only aid in the process of language revival but also offer community members interactive and engaging platforms that allow them to meaningfully reconnect with their language. Combining linguistic, cultural, and technological aspects is a complete technique that demonstrates how to preserve and revitalize endangered languages.
The interplay between language preservation and revitalization is dynamic, fostering a holistic approach to safeguarding linguistic diversity. As preservation efforts lay the groundwork by documenting and conserving linguistic resources, revival initiatives breathe life back into endangered languages, showcasing the resilience and adaptability of linguistic heritage. This cyclical relationship underscores the importance of a comprehensive and collaborative approach involving diverse expertise and creative methodologies to ensure the enduring vitality of endangered languages.
How AI is becoming one of the Pillars for Language Preservation
Artificial intelligence has the potential to transform language preservation efforts by developing new tools and methods for recording, preserving, and sustaining endangered languages. The use of artificial intelligence in language preservation can considerably increase the efficiency and effectiveness of such projects. This section examines how artificial intelligence (AI) can be used to document, translate, and transcribe endangered languages, as well as teach and study them. Documenting endangered languages is critical to conserving linguistic diversity and cultural heritage. Artificial intelligence (AI) emerges as a strong friend in this effort, delivering innovative technologies that speed data collecting, processing, and archiving. Here are several ways in which AI can significantly contribute to the documentation of endangered languages(as shown in Fig 1):
- Speech Recognition- Automated Transcription: AI-powered speech recognition algorithms excel at converting spoken language into text. This automation facilitates the transcription of oral traditions, narratives, and conversations, making it more efficient to document and analyze spoken language elements.
- Text Analysis- Linguistic Data Extraction: AI algorithms can analyze enormous amounts of text data, including documents, corpora, and historical records. This enables them to extract important linguistic information, identify patterns, and uncover underlying language structures, so assisting researchers.
- Image and Audio Processing- Text and Phonetic Extraction: AI technologies can process images containing handwritten texts or audio recordings to extract pertinent linguistic data. This includes identifying and transcribing handwritten manuscripts and extracting phonetic information from audio samples, enriching the corpus of documented language elements.
- Data Organization and Archiving- Structured Databases: AI plays a role in organizing and categorizing linguistic data, creating structured databases for efficient archiving. This ensures that the collected data is well-organized, easily accessible, and searchable. AI-driven archiving solutions contribute to the preservation of linguistic knowledge for researchers and language communities.
- Language Revitalization Support- Language Learning Apps: AI can aid in the development of language learning applications that cater specifically to endangered languages. These apps can provide interactive and personalized learning experiences, contributing to language revitalization efforts within communities.
By harnessing the capabilities of AI, researchers and linguists can accelerate the documentation process of endangering languages. The efficiency and accuracy of AI-powered tools enable a more comprehensive understanding of linguistic nuances, ensuring that valuable knowledge is preserved before it succumbs to the threat of extinction. The synergy between AI and linguistic preservation serves as a testament to the potential of technology in safeguarding the cultural heritage embedded in endangered languages for future generations.
How AI is helpful in Translating and Transcribing
AI plays a transformative role in enhancing language preservation through its contributions to translation [8] and transcription processes. Here’s how AI can significantly impact these critical aspects of language preservation (as shown in Fig 2):
- Machine Translation – Neural Machine Translation: AI-powered machine translation systems, particularly those utilizing neural networks, have revolutionized the translation landscape. These systems can automatically translate texts from one language to another, learning from extensive bilingual datasets to improve accuracy and fluency over time. This is particularly valuable for preserving endangered languages by making their content accessible to a wider audience.
- Efficient Transcription: AI algorithms excel at converting spoken words to written text. This is especially useful for languages with little written materials and documentation. Automatic transcription speeds up the process of establishing written records for endangered languages, hence facilitating their preservation and dissemination.
- Real-Time Translation and Transcription – Speech to Speech Translation: AI-accelerated advancements, such as speech-to-speech translation systems, enable real-time translation between speakers of different languages. This is immensely valuable for language preservation efforts, as it facilitates immediate communication and collaboration among diverse linguistic communities.
- Live Transcription: AI-driven live transcription tools provide the capability to transcribe spoken language in real-time. This can be particularly useful in documenting oral traditions, interviews, or community events, ensuring accurate and timely preservation of linguistic content.
- Time and Resource Efficiency – AI improves time and resource efficiency in translation and transcribing, streamlining documentation. Automated systems can efficiently manage vast amounts of linguistic content, speeding up the documenting of endangered languages and contributing to the production of complete linguistic records.
- Accessibility and Inclusivity – Bridging Communication Gaps: AI-powered translation and transcription technologies enhance accessibility and inclusivity in language preservation. By enabling real-time communication and transcription across different languages, these tools contribute to more inclusive documentation practices and collaborative efforts within linguistic communities.
In conclusion, AI’s contributions to language translation and transcription represent a powerful asset in the preservation of endangered languages. These technologies not only enhance the efficiency of documentation processes but also bridge communication gaps, making linguistic content more accessible and fostering collaboration among diverse linguistic communities. The integration of AI in language preservation aligns with the overarching goal of ensuring the continued vitality and accessibility of endangered languages for future generations.
Provides a way to learn the Extinct Languages
By transforming the methods of teaching and learning, artificial intelligence (AI) plays a critical role in solving the problem of passing on endangered languages to future generations. Artificial intelligence (AI)-powered interactive language learning platforms use adaptive learning strategies to customize the experience to each user’s requirements and expertise level. Effective learning and engagement are ensured by this individualized approach. AI also brings in creativity components, which enhance language learning with engaging games that promote grammar, vocabulary, and cultural aspects. Real-time pronunciation feedback is provided by speech recognition technology, which helps students improve their accuracy and confidence when speaking. AI-powered intelligent tutoring systems provide individualized instruction in grammar comprehension, sentence structure understanding, and vocabulary learning. Through interactive exercises and language learning apps, these systems improve language acquisition overall.
By overcoming geographical challenges and reaching a worldwide audience through digital platforms and applications, artificial intelligence (AI) in language learning also supports accessibility for endangered languages. Additionally, AI creates a variety of culturally appropriate content, such folktales and storytelling, which enhances the learning process and helps students develop a stronger bond with the language’s cultural background. AI-powered language learning tools help to revitalize endangered languages by incorporating cultural context, history, and customs into the curriculum. This instills a sense of cultural pride and motivation among learners. In general, the use of AI in educational activities related to endangered languages is consistent with the overarching objective of safeguarding linguistic variety and cultural legacy.
By creating interactive, adaptive, and engaging language learning experiences, AI serves as a catalyst for safeguarding the spread of endangered languages to future generations, fostering renewed interest and appreciation for linguistic and cultural richness.
Part of AI in the Language Revival World
Artificial intelligence (AI) has the possibility to make a substantial contribution to the difficult task of restoring ancient languages. AI has the potential to be extremely helpful in bringing extinct languages back to life by supporting their comeback, creating educational resources, and helping with their reconstruction. One method uses artificial intelligence (AI) to reconstruct languages that have vanished by studying historical documents, related languages, and current linguistic resources. AI algorithms are very good at pattern recognition, which enables them to identify linguistic patterns and deduce the syntax, phonology, and grammar of extinct languages. Based on existing linguistic data, AI can create prototypes of extinct languages through automated language modeling, laying the groundwork for future study and experimentation in attempts to revive lost languages.
AI also makes it easier to compare languages with similar contexts, recognizing cognates, common lexicon, and grammatical patterns that help guide the reconstruction process. Academics and linguists can contribute to the preservation of linguistic heritage and speed up the process of reviving extinct languages by utilizing AI technology. This will also create the foundation for language revival initiatives.
AI provides learning material to revive the language
After an ancient language has been reconstructed, the next stage is to develop instructional resources that allow people to learn and use the language. AI has the potential to significantly improve the accessibility, interest, and utility of these resources. Here’s how artificial intelligence (AI) might assist create instructional materials for students.
a) AI-powered natural language processing can generate sentences, exercises, and drills based on reconstructed language grammar and vocabulary. This can help learners practice and strengthen their language skills.
b) AI can generate audio samples of the revived language, allowing students to practice pronunciation. Text-to-speech synthesis can give students with accurate and natural-sounding voice samples.
Conclusion
The future of AI in languages preservation and rebirth offers possibilities, as do developing technologies such as neural machine translation, speech recognition, and natural language generation. These advancements offer efficient documentation, enhanced language learning experiences, and even the recovery of more extinct languages. However, challenges such as data availability and ethical considerations must be investigated. AI’s role in language preservation relies on close collaboration between researchers and linguists, ensuring cultural sensitivity, responsible application, and inclusive practices. As AI continues to evolve, it presents futuristic solutions to the problems in the preservation and revival of endangered languages, contributing to a more diversified heritage for future generations.
References
- Chhabra, A., Singh, S. K., Sharma, A., Kumar, S., Gupta, B. B., Arya, V., & Chui, K. T. (2024). Sustainable and intelligent time-series models for epidemic disease forecasting and analysis. Sustainable Technology and Entrepreneurship, 3(2), 100064.
- Singh, R., Singh, S. K., Kumar, S., & Gill, S. S. (2022). SDN-Aided Edge Computing-Enabled AI for IoT and Smart Cities. SDN-Supported Edge-Cloud Interplay for Next Generation Internet of Things, 41-70.
- Kumar, S., Singh, S. K., Aggarwal, N., Gupta, B. B., Alhalabi, W., & Band, S. S. (2022). An efficient hardware supported and parallelization architecture for intelligent systems to overcome speculative overheads. International Journal of Intelligent Systems, 37(12), 11764-11790.
- Singh, I., Singh, S. K., Singh, R., & Kumar, S. (2022, May). Efficient loop unrolling factor prediction algorithm using machine learning models. In 2022 3rd International Conference for Emerging Technology (INCET) (pp. 1-8). IEEE.
- Peñalvo, F. J. G., Maan, T., Singh, S. K., Kumar, S., Arya, V., Chui, K. T., & Singh, G. P. (2022). Sustainable Stock Market Prediction Framework Using Machine Learning Models. International Journal of Software Science and Computational Intelligence (IJSSCI), 14(1), 1-15.
- Mengi, G., Singh, S. K., Kumar, S., Mahto, D., & Sharma, A. (2021, September). Automated Machine Learning (AutoML): The Future of Computational Intelligence. In International Conference on Cyber Security, Privacy and Networking (pp. 309-317). Cham: Springer International Publishing.
- Sudhakar, & Kumar, S. (2023, April). ABBDIoT: Anomaly-Based Botnet Detection Using Machine Learning Model in the Internet of Things Network. In International Conference on IoT, Intelligent Computing and Security: Select Proceedings of IICS 2021 (pp. 235-245). Singapore: Springer Nature Singapore.
- Verma, V., Benjwal, A., Chhabra, A., Singh, S. K., Kumar, S., Gupta, B. B., … & Chui, K. T. (2023). A novel hybrid model integrating MFCC and acoustic parameters for voice disorder detection. Scientific Reports, 13(1), 22719.
- Gupta, S., Agrawal, S., Singh, S. K., & Kumar, S. (2023). A Novel Transfer Learning-Based Model for Ultrasound Breast Cancer Image Classification. In Computational Vision and Bio-Inspired Computing: Proceedings of ICCVBIC 2022 (pp. 511-523). Singapore: Springer Nature Singapore.
- Sharma, A., Singh, S. K., Chhabra, A., Kumar, S., Arya, V., & Moslehpour, M. (2023). A Novel Deep Federated Learning-Based Model to Enhance Privacy in Critical Infrastructure Systems. International Journal of Software Science and Computational Intelligence (IJSSCI), 15(1), 1-23.
Cited By
Gupta C, Sharma A, (2024) Reviving Indigenous Languages using Machine Learning, Insights2Techinfo, pp.1