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 des matières
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 Co Re Poo L–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
A propos de l’auteur
L. Ashok Kumar, Ph D, is a professor in the Department of Electrical and Electronics Engineering, PSG of Technology, Tamil Nadu, India. He has published more than 175 papers in international and national journals and received 26 awards for his Ph D project on wearable electronics at national and international levels. He has created eight Centres of Excellence at PSG in collaboration with government agencies and industries such as the Centre for Audio Visual Speech Recognition and the Centre for Excellence in Solar Thermal Systems. Twenty-three out of 27 of his products have been technologically transferred to government funding agencies.
D. Karthika Renuka, Ph D, is a professor at PSG of Technology, Tamil Nadu, India. Her main areas of study focus on data mining, evolutionary algorithms, and machine learning. She is a recipient of the Indo-U.S. Fellowship for Women in STEMM. She has organized two international conferences on The Innovation of Computing Techniques and Information Processing and Remote Computing.
Bharathi Raja Chakravarthi, Ph D, is an assistant professor in the School of Computer Science, University of Galway, Ireland. His studies focus on multimodal machine learning, abusive/offensive language detection, bias in natural language processing tasks, inclusive language detection, and multilingualism. He has published many papers in international journals and conferences. He is an associate editor of the journal Expert System with Application and an editorial board member for Computer Speech & Language.
Thomas Mandl, Ph D, is a professor of Information Science and Language Technology, University of Hildesheim, Germany. His research interests include information retrieval, human-computer interaction, and internationalization of information technology and he has published more than 300 papers on these topics. He coordinated tracks at the Cross Language Evaluation Forum (CLEF), the European information retrieval evaluation initiative. Thomas Mandl is the co-chair at FIRE, the evaluation initiative for Indian languages, since 2020 and coordinates the HASOC track on hate speech detection.
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