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.

Table of Contents:
Foreword xix Preface xxi Acknowledgement xxiii 1 A Hybrid Deep Learning Model for Emotion Conversion in Tamil Language 1 Satrughan Kumar Singh, Muniyan Sundararajan and Jainath Yadav 2 Attention-Based End-to-End Automatic Speech Recognition System for Vulnerable Individuals in Tamil 15 S. Suhasini, B. Bharathi and Bharathi Raja Chakravarthi 3 Speech-Based Dialect Identification for Tamil 27 Archana J.P. and B. Bharathi 4 Language Identification Using Speech Denoising Techniques: A Review 41 Amal Kumar, Piyush Kumar Singh and Jainath Yadav 5 Domain Adaptation-Based Self-Supervised ASR Models for Low-Resource Target Domain 51 L. Ashok Kumar, D. Karthika Renuka, Naveena K. S. and Sree Resmi S. 6 ASR Models from Conventional Statistical Models to Transformers and Transfer Learning 69 Elizabeth Sherly, Leena G. Pillai and Kavya Manohar 7 Syllable-Level Morphological Segmentation of Kannada and Tulu Words 113 Asha Hegde and Hosahalli Lakshmaiah Shashirekha 8 A New Robust Deep Learning-Based Automatic Speech Recognition and Machine Transition Model for Tamil and Gujarati 135 Monesh Kumar M. K., Valliammai V., Geraldine Bessie Amali D. and Mathew M. Noel 9 Forensic Voice Comparison Approaches for Low-Resource Languages 155 Kruthika S.G., Trisiladevi C. Nagavi and P. Mahesha 10 CoRePooL--Corpus for Resource-Poor Languages: Badaga Speech Corpus 193 Barathi Ganesh H.B., Jyothish Lal G., Jairam R., Soman K.P., Kamal N.S. and Sharmila B. 11 Bridging the Linguistic Gap: A Deep Learning-Based Image- to-Text Converter for Ancient Tamil with Web Interface 213 S. Umamaheswari, G. Gowtham and K. Harikumar 12 Voice Cloning for Low-Resource Languages: Investigating the Prospects for Tamil 243 Vishnu Radhakrishnan, Aadharsh Aadhithya A., Jayanth Mohan, Visweswaran M., Jyothish Lal G. and Premjith B. 13 Transformer-Based Multilingual Automatic Speech Recognition (ASR) Model for Dravidian Languages 259 Divi Eswar Chowdary, Rahul Ganesan, Harsha Dabbara, G. Jyothish Lal and Premjith B. 14 Language Detection Based on Audio for Indian Languages 275 Amogh A. M., A. Hari Priya, Thanvitha Sai Kanchumarti, Likhitha Ram Bommilla and Rajeshkannan Regunathan 15 Strategies for Corpus Development for Low-Resource Languages: Insights from Nepal 297 Bal Krishna Bal, Balaram Prasain, Rupak Raj Ghimire and Praveen Acharya 16 Deep Neural Machine Translation (DNMT): Hybrid Deep Learning Architecture-Based English-to-Indian Language Translation 331 Nivaashini M., Priyanka G. and Aarthi S. 17 Multiview Learning-Based Speech Recognition for Low-Resource Languages 375 Aditya Kumar and Jainath Yadav 18 Automatic Speech Recognition Based on Improved Deep Learning 405 Kingston Pal Thamburaj and Kartheges Ponniah 19 Comprehensive Analysis of State-of-the-Art Approaches for Speaker Diarization 427 Trisiladevi C. Nagavi, Samanvitha S., Shreya Sudhanva, Sukirth Shivakumar and Vibha Hullur 20 Spoken Language Translation in Low-Resource Language 445 S. Shoba, Sasithradevi A. and S. Deepa References 456