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Record Nr. |
UNINA9910851996903321 |
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Autore |
Mohanty Sushree Sangita |
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Titolo |
Applying AI-Based Tools and Technologies Towards Revitalization of Indigenous and Endangered Languages |
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Pubbl/distr/stampa |
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Singapore : , : Springer Singapore Pte. Limited, , 2024 |
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©2024 |
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ISBN |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (221 pages) |
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Collana |
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Studies in Computational Intelligence Series ; ; v.1148 |
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Altri autori (Persone) |
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DashSatya Ranjan |
ParidaShantipriya |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Intro -- Preface -- Contents -- About the Editors -- Language Revitalization & -- Artificial Intelligence -- Kuvi Character Set: A Mobile Interface for the Revitalization of the Kuvi Language -- 1 Introduction: The Imperative Need for Developing a Kuvi Character Set for an Unwritten Endangered Language -- 1.1 About the Speaker -- 1.2 Historical Background of Kuvi Language -- 2 KISS Model of Character Set Development -- 2.1 Participants -- 2.2 Phases -- 2.3 Process -- 2.4 Principles -- 3 Summary -- References -- Reviving Endangered Languages: Exploring AI Technologies for the Preservation of Tanzania's Hehe Language -- 1 Introduction -- 2 Literature Review -- 3 Proposed Model -- 4 Conclusion and Future Prospect -- References -- Preservation of Vedda's Language in Sri Lanka -- 1 Introduction -- 1.1 About the Language -- 1.2 Challenges and Opportunities -- 2 Literature Survey -- 3 Propose Model -- 4 Preserve and Promote the Vedda Language -- 5 Conclusion and Future Work -- References -- Role of Digital Technology in the Education, Promotion, and Revitalization of "Ho" Languages -- 1 Introduction -- 2 Methodology -- 3 Role of Digital Technology -- 3.1 Indigenous Communities and Technology -- 4 Proposed Digital Technology for Ho Language -- 4.1 Different Factors for Promotion of Ho Language -- 5 Revitalization of Ho Language -- 5.1 Technology in Endangered Language Contexts -- 6 Ho Language Education -- 6.1 Documentation, |
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Preservation, and Revitalization -- 6.2 Language Pedagogy -- 7 Conclusion -- References -- Changing the Trajectory: Preserving the Linguistic Diversity of Shi Language Using AI and NLP -- 1 Introduction -- 2 The Shi Language: Overview and Challenges -- 2.1 Linguistic Characteristics of Shi Language -- 2.2 Language Endangerment Factors -- 2.3 Sociocultural Implications -- 3 AI and NLP in Language Revitalization. |
3.1 Role of AI in Language Preservation -- 3.2 NLP Applications for Endangered Languages -- 4 Future Prospects -- 4.1 Data Collection and Analysis -- 4.2 Community Engagement and Collaboration -- 5 AI-Based Solutions for Shi Language Revitalization -- 5.1 Automatic Speech Recognition (ASR) Systems -- 5.2 Machine Translation and Language Generation -- 5.3 Language Learning Applications -- 5.4 Digital Archives and Preservation -- 6 Ethical Considerations and Cultural Sensitivity -- 6.1 Informed Consent and Community Involvement -- 6.2 Preserving Cultural Nuances and Context -- 6.3 Balancing Technological Advancements with Traditional Knowledge -- 7 Case Studies of AI Implementation in Language Revitalization -- 7.1 Impact Assessment and Evaluation -- 8 Future Directions and Recommendations -- 8.1 Long-Term Sustainability Strategies -- 8.2 Collaboration with Indigenous Communities -- 8.3 Policy and Funding Support -- 9 Conclusion -- References -- Kuvi Calendar: Harnessing Indigenous Calendar for Language Revitalization -- 1 Introduction: Understanding the Cultural and Practical Significance of Kuvi Calendar -- 1.1 The Cultural and Practical Importance of Indigenous Calendar -- 2 Process of Making Kuvi Calendar -- 2.1 The Multi-Step Process for the Development of Kuvi (Physical) Calendar First Phase -- 2.2 The Second Phase-Development of Parallel Corpus for Kuvi (Digital) Calendar -- 3 Embodying Kuvi Cultural Heritage: The Physical Kuvi Calendar -- 3.1 Week Structure -- 3.2 Month Structure -- 3.3 Day Structure in Each Month -- 4 Conclusion -- References -- Natural Language Process (NLP) for Language Analysis -- Contemplating Dialects When Building a Guarani Corpus for NLP -- 1 Introduction -- 2 Minority Languages in South America -- 2.1 Brief Socio-historical Background of Guarani in Paraguay -- 2.2 Guarani Features. |
3 Challenges Faced While Building a Guarani-Spanish Corpus -- 3.1 Challenge 1: Lack of Data to Build a Corpus -- 3.2 Challenge 2: Guarani and Spanish Meet in Jopara -- 3.3 Challenge 3: The Unbearable Lightness of Guarani Orthography -- 4 Conclusion and Future Prospect -- References -- The Role of NLP to Facilitate the Growth of Ge'ez Language -- 1 Introduction -- 1.1 About the Ge'ez Language -- 1.2 Number of Speakers -- 2 Literature Review -- 2.1 The Role of NLP to Facilitate the Growth of Ge'ez Language -- 2.2 Applications of NLP -- 3 Conclusion -- 4 Future Work -- References -- Developing Multilingual Glossaries for STEM Terminology Using AI-NLP -- 1 Introduction -- 2 Building the Glossary -- 3 AI-Mediated NLP-Based Word Creation -- 4 Conclusion and Future Perspectives -- References -- Development of Parallel Speech Data Repository for Ho Language -- 1 Introduction -- 2 Literature Survey -- 3 Proposed Model -- 3.1 Digital Resources -- 3.2 Data Scraping from Ho Wikipedia -- 3.3 Optical Character Recognition -- 3.4 Parallel Corpus -- 3.5 Manually Correction from Human Volunteers -- 3.6 Speech to Text -- 4 Conclusion -- References -- Challenges to Prepare the Parallel Corpus for Luganda Language -- 1 Introduction -- 2 Literature Survey -- 3 Proposed Model -- 3.1 Optical Character Recognition -- 3.2 Speech to Text -- 3.3 Web Scraping -- 3.4 Newspapers -- 4 Conclusion and Future Work -- References -- Proposed Model for Automatic |
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Dialect Classification of Binjhal Language -- 1 Introduction -- 2 Binjhal Language -- 2.1 Language Identification -- 3 Literature Reviews -- 4 Data Collection and Preparation -- 5 Proposed Model -- 5.1 Preprocessing -- 5.2 Types of Preprocessing -- 6 Experiment Result and Evaluation -- 7 Conclusion -- References -- Twi Speech Processing: Techniques and Applications -- 1 Introduction -- 2 Literature Review. |
2.1 Twi Language and Linguistic Characteristics -- 2.2 Challenges in Twi Speech Processing -- 2.3 Techniques in Twi Speech Processing -- 2.4 Applications of Twi Speech Processing -- 2.5 Future Directions in Twi Speech Processing -- 3 Methodology -- 3.1 Data Collection and Preprocessing -- 3.2 Feature Extraction -- 3.3 Speech Processing Applications -- 3.4 Dialectal Variations Analysis -- 3.5 Speaker Identification and Verification -- 3.6 Evaluation and Validation -- 3.7 Future Directions -- 4 Techniques and Working Principle -- 5 Conclusion and Future Directions -- References -- Cultural Survival Heritage of Bambara Language by Using NLP -- 1 Introduction -- 2 Literature Survey -- 3 Cultural Significance of Bambara Language in Malian Literature and Music -- 4 Socio-Cultural Factors Impacting the Preservation of Bambara -- 5 Language Revitalization Efforts in Mali -- 6 AI-Based Language Documentation Projects for Endangered Languages -- 7 Government Policies and International Cooperation for Language Preservation -- 8 NLP-Based Language Revitalization Projects in Other Regions -- 9 Proposed Model -- 10 Data Collection of Bambara Texts -- 11 Machine Learning (Clustering the Collected Bambara Texts) -- 12 Data Processing (Tokenization and Stemming) -- 13 Sentiment Analysis (Optional) -- 14 Model Evaluation -- 15 Feature Extraction -- 16 Result and Application -- 17 Conclusion -- References -- Dialect Identification of Gondar, Gojjami, and Showa Language of Amharic Using AI and NLP -- 1 Introduction -- 2 Literature Survey -- 2.1 Literature Survey -- 2.2 Tigrinya Dialect Identification -- 2.3 Assamese Dialects -- 2.4 Santali Dialect Identification -- 2.5 Kamrupi Dialect Identification -- 2.6 Maghrebian Dialect Recognition -- 2.7 Algerian Dialect Recognition -- 2.8 Tunisian Dialect Recognition -- 2.9 Goalparia Dialect Identification. |
2.10 Ao Dialect Identification -- 3 Proposed Method -- 3.1 Data Collection and Preprocessing -- 3.2 Data Collection and Preprocessing -- 4 Results and Discussions -- 4.1 Model Performance -- 4.2 Challenges and Limitations -- 4.3 Future Directions -- 5 Conclusion -- References -- Creating a Parallel Corpus for Machine Translation: A Case Study of Kru and Krio -- 1 Introduction -- 1.1 Krio -- 1.2 Kru -- 1.3 Syntax and Alphabet -- 2 Related Works -- 2.1 Works Done on Similar Languages -- 3 Proposed Model -- 3.1 Optical Character Recognition -- 3.2 Books -- 3.3 Existing Database -- 3.4 Web Scraping -- 3.5 Speech-To-Text -- 4 Conclusion -- 5 Future Works -- References -- Developing Parallel Corpus for the Machine Translation System in Dzongkha Language -- 1 Introduction -- 2 Literature Review -- 3 Proposed Model -- 4 Conclusion and Future Prospects -- References. |
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