Motivation and Scope of the Book

Out of over 7000 recognized languages worldwide, only a small proportion offer sufficient resources to build speech and natural language processing (NLP) technologies adequately. Developing such solutions for low-resource languages is challenging in multiple aspects. The dependency on deep learning over volumes of resources is quite conventional. Hence, the research in this domain is propelled by different ideas to train and validate systems, such as data augmentation, transfer learning, and hybrid multi-modal architectures, to name a few. This book aims at collecting such ideas, advent, and solutions for building speech/NLP technologies in low-resource scenarios.

Timeline

The submission guidelines and other detailed information can be found on the book website.

• Full Chapter Abstract Submission:                 10.01.2024 

• Abstract Acceptance/Rejection Notification:   25.01.2024

• Full Chapter Submission:                               15.03.2024

• First Review Notification:                               15.04.2024

• Revised Version Notification:                         15.05.2024

• Final Acceptance/Rejection Notification:       30.05.2024

• Camera Ready Chapter Submission:           10.06.2024

Table of Contents

We invite submissions of high-quality, original chapters addressing both theoretical and practical aspects, including their ethical and social implications of NLP in healthcare. The Book aims to cover (but is not limited) the following topics:

• Speech Processing for Low Resource Languages

• Natural Language Processing for Low Resource

• Deep learning methods for low-resource languages

• End-to-end speech Recognition for Low-Resource Language

• Development of a Speech Corpus for Low Resource Language

• Multimodal architectures for social media analysis

• Speech Recognition for Specific Dialects

• Speech synthesis for low-resource language

• Speech translation for low-resource language

• Indigenous Language revitalization/preservation

• Transfer learning applications

• Leveraging Large pre-trained language models knowledge under few-shot and zero-shot in NLP tasks

• Efficiently aligning acoustic and textual embeddings

Submission Guidelines

Authors should send their abstracts and chapters/articles through easy-chair only.

• Abstract: The abstract must contain 300-400 words including title, chapter outline/content, and author (s) affiliation.

• Chapter/article: Each chapter/article must contain 15-25 pages with approximately 9,500-12,500 words including references (contact the editors for additional page approval). Each chapter/article must be compliant with the alt-text format. If you need more details, don't hesitate to contact the editors. 

Editors

• Dr. Shantipriya Parida (Silo AI, Finland)
• Assoc. Prof. Satya Ranjan Dash (KIIT University, India)
• Asst. Prof. Biswa Ranjan Acharya (Marwadi university, India)
• Dr. Ravi Shankar Prasad (Idiap Research Institute, Switzerland)
• Prof. Esaú Villatoro-Tello (Idiap Research Institute, Switzerland)

Contact

All questions about submissions should be emailed to speech_nlp_book_river@googlegroups.com