CALL FOR BOOK CHAPTERS BOOK TITLE: Automatic Speech Recognition and Translation for Low Resource Languages

[Bharathi Raja Asoka Chakravarthi]

CALL FOR BOOK CHAPTERS

BOOK TITLE: Automatic Speech Recognition and Translation for Low Resource Languages

Speech is a fundamental means of communication for humans. Speech is a natural interface for communicating with smart devices. Computational Linguistics is a broad field which includes language understanding. Research on Automatic Speech Recognition focuses on improving the ability of computers to understand and process human languages (both spoken and written). This is all possible because of the remarkable development of machine learning and artificial intelligence methods that accurately read both spoken and written language. In today's computer and mobile era, speech has become a more important channel for human-machine connection. Human-computer interaction has been evolving since the dawn of computer engineering. For more than 60 years, automatic speech recognition has been a subject of study. The transcription industry has evolved tons over the past 10 years. However, in practical acoustic settings, accurately understanding spoken utterances remains a challenge. Artificial Intelligence (AI), Deep Learning (DL), and Natural Language Processing (NLP) are enabling the development of speech recognition and machine translation techniques. It facilitates accurate analysis of text and speech, as well as more natural communication between humans and computers in all human languages. Computational linguistics, NLP, computing, mathematics, speech processing, ML, acoustics, and other fields can all benefit greatly from these methods. Speech recognition applications have reached the pinnacle of success after the advent of neural network architectures. Many challenges in the conventional speech recognition systems are effortlessly addressed by deep learning models. Machine translation of both text and voice is another significant use for this technology. Because of their limited computing power, low-resource languages lag far behind in voice and language processing. Numerous language processing issues can be solved in real time with improved user experience and efficiency thanks to access to the enormous number of computational sources from diverse digital sources. Technologies for processing speech and language in languages with few available resources are just being started. The relevance of these languages cannot be overstated, and research in this area will increase the probability of their becoming a functional part of our daily lives. In addition, the cultural shift toward digital media has coincided with spectacular advancements in digital media, computing power, computational storage, and software capabilities, all of which have inspired the hope of translating low-resource computing language resources into efficient computing models. Opportunities in low-resource speech recognition have been expanded by recent developments in artificial intelligence, such as automatic speech recognition (ASR) in low-resource languages, self-supervised, zero shot and transfer learning, enhanced deep-learning architectures. The goal of this book is to delve into the emerging field of computational models for processing language, speech, and text. Low-resource languages are the focus of the fresh and innovative solutions, which aim to improve content development, knowledge management, and more natural communication

 

 Topics of Interest:

Section 1: Speech Recognition

·Fundamentals of Speech and Components of speech recognition technology

·Intensive review on low resource corpus and toolkits for speech recognition

·ASR models from conventional statistical models to transformers to transfer learning

·Code switching techniques for cross lingual ASR system

·Recent Advances in robust speech recognition and denoising techniques for noisy speech

·Cutting edge approaches in language modelling

·End-to-End ASR systems in low resource languages

·Recent advances in self-supervised learning for ASE.

·Multilingual ASR

·Zeroshot learning for Automatic Speech Recognition

· Recent advances in streaming ASR system in low resource languages              

 

Section 2: Language Translation

·Neural network based machine translation techniques

·Contextual analysis on embedding techniques for low resource machine translation applications

· Analysis of Natural Language Processing toolkits, Corpus for low resource language

·Spoken Language Translation in low resource languages

· Recent advances in domain specific machine translation system

·State of the art pretrained models in spoken language translation

·Techniques to improve generalization and interpretability of neural network based Machine translation models

·Recent advances in self-supervised and transfer learning approaches for machine translation

·Recent advances in zero shot and few shot translation.

 Important Dates:

Abstract Submission (of approx. 500 words):28.02.2023        

Abstract Acceptance : 15.03.2023

Full Chapter Submission : 15.04.2023

Chapter Acceptance : 15.06.2023

Final chapter Submission (in Word): 30.06.2023       

Submission to Publisher : 31.07.2023

Editors:

· Dr.L.Ashok Kumar,Professor, Dept.  of EEE,PSG College of Technology, Coimbatore, India

· Dr.D.Karthika Renuka, Professor, Dept.  of IT,  PSG College of Technology, Coimbatore, India

·Dr. Bharathi Raja Chakravarthi, Assistant Professor / Lecturer-above-the-bar School of Computer Science, University of Galway, Ireland

·Dr.Thomas Mandl, Professor, Institute for Information Science and Language Technology, University of Hildesheim, Hildesheim, Germany

  

Authors are invited to submit chapters (around 200-250 words) stating the objective and structure of the unpublished research work to the email id: asrw...@gmail.com . 

The book will be published under the Wiley-Scrivener imprint and will be indexed by Scopus and offered to Web of Science.

Note: “There are NO processing/publication charges for this book”

Thanks and Regards

Editorial Team