Automatic Speech Recognition and Translation for Low Resource Languages

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.

Auteur

L. Ashok Kumar, PhD, 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 PhD 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, PhD, 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, PhD, 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, PhD, 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.