Automatic Speech Recognition and Translation for Low Resource Languages
eBook - PDF

Automatic Speech Recognition and Translation for Low Resource Languages

  1. English
  2. PDF
  3. Available on iOS & Android
eBook - PDF

Automatic Speech Recognition and Translation for Low Resource Languages

About this book

AUTOMATIC SPEECH RECOGNITION and TRANSLATION for LOW-RESOURCE LANGUAGES

This book is a comprehensive exploration into the cutting-edge research, methodologies, and advancements in addressing the unique challenges associated with ASR and translation for low-resource languages.

Automatic Speech Recognition and Translation for Low Resource Languages contains groundbreaking research from experts and researchers sharing innovative solutions that address language challenges in low-resource environments. The book begins by delving into the fundamental concepts of ASR and translation, providing readers with a solid foundation for understanding the subsequent chapters. It then explores the intricacies of low-resource languages, analyzing the factors that contribute to their challenges and the significance of developing tailored solutions to overcome them.

The chapters encompass a wide range of topics, ranging from both the theoretical and practical aspects of ASR and translation for low-resource languages. The book discusses data augmentation techniques, transfer learning, and multilingual training approaches that leverage the power of existing linguistic resources to improve accuracy and performance. Additionally, it investigates the possibilities offered by unsupervised and semi-supervised learning, as well as the benefits of active learning and crowdsourcing in enriching the training data. Throughout the book, emphasis is placed on the importance of considering the cultural and linguistic context of low-resource languages, recognizing the unique nuances and intricacies that influence accurate ASR and translation. Furthermore, the book explores the potential impact of these technologies in various domains, such as healthcare, education, and commerce, empowering individuals and communities by breaking down language barriers.

Audience

The book targets researchers and professionals in the fields of natural language processing, computational linguistics, and speech technology. It will also be of interest to engineers, linguists, and individuals in industries and organizations working on cross-lingual communication, accessibility, and global connectivity.

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Yes, you can access Automatic Speech Recognition and Translation for Low Resource Languages by L. Ashok Kumar,D. Karthika Renuka,Bharathi Raja Chakravarthi,Thomas Mandl in PDF and/or ePUB format, as well as other popular books in Computer Science & Artificial Intelligence (AI) & Semantics. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Dedication Page
  5. Contents
  6. Foreword
  7. Preface
  8. Acknowledgement
  9. Chapter 1 A Hybrid Deep Learning Model for Emotion Conversion in Tamil Language
  10. Chapter 2 Attention-Based End-to-End Automatic Speech Recognition System for Vulnerable Individuals in Tamil
  11. Chapter 3 Speech-Based Dialect Identification for Tamil
  12. Chapter 4 Language Identification Using Speech Denoising Techniques: A Review
  13. Chapter 5 Domain Adaptation-Based Self-Supervised ASR Models for Low-Resource Target Domain
  14. Chapter 6 ASR Models from Conventional Statistical Models to Transformers and Transfer Learning
  15. Chapter 7 Syllable-Level Morphological Segmentation of Kannada and Tulu Words
  16. Chapter 8 A New Robust Deep Learning-Based Automatic Speech Recognition and Machine Transition Model for Tamil and Gujarati
  17. Chapter 9 Forensic Voice Comparison Approaches for Low-Resource Languages
  18. Chapter 10 CoRePooL—Corpus for Resource-Poor Languages: Badaga Speech Corpus
  19. Chapter 11 Bridging the Linguistic Gap: A Deep Learning-Based Imageto-Text Converter for Ancient Tamil with Web Interface
  20. Chapter 12 Voice Cloning for Low-Resource Languages: Investigating the Prospects for Tamil
  21. Chapter 13 Transformer-Based Multilingual Automatic Speech Recognition (ASR) Model for Dravidian Languages
  22. Chapter 14 Language Detection Based on Audio for Indian Languages
  23. Chapter 15 Strategies for Corpus Development for Low-Resource Languages: Insights from Nepal
  24. Chapter 16 Deep Neural Machine Translation (DNMT): Hybrid Deep Learning Architecture-Based English-to-Indian Language Transla ion
  25. Chapter 17 Multiview Learning-Based Speech Recognition for Low-Resource Languages
  26. Chapter 18 Automatic Speech Recognition Based on Improved Deep Learning
  27. Chapter 19 Comprehensive Analysis of State-of-the-Art Approaches for Speaker Diarization
  28. Chapter 20 Spoken Language Translation in Low-Resource Language
  29. Index
  30. EULA