Deep Learning Approaches for Spoken and Natural Language Processing

This book provides insights into how deep learning techniques impact language and speech processing applications, covering the promise, limits, and new challenges. It presents a comprehensive survey of major applications and research-oriented articles based on deep learning techniques, relevant to academicians, research scholars, industrial experts, scientists, and post-graduate students.

Format: Hardback
Length: 165 pages
Publication date: 02 December 2021
Publisher: Springer Nature Switzerland AG

This book delves into the profound impact of deep learning techniques on language and speech processing applications. The authors explore the promising prospects, inherent limitations, and emerging challenges associated with this cutting-edge technology. Spanning a wide range of applications, the book distinguishes between the various implementations of deep learning and classical machine learning techniques. Its primary objective is to present a comprehensive survey of prominent applications and research-oriented articles centered around deep learning techniques, specifically focused on natural language and speech signal processing. This book holds immense relevance for academicians, research scholars, industry experts, scientists, and postgraduate students engaged in the fields of speech signal and natural language processing. By incorporating deep learning, these individuals aim to enhance the capabilities and effectiveness of their work.

The book begins by discussing the current research challenges and future perspectives regarding the application of deep learning to enhance NLP and speech processing applications. It sheds light on the potential of deep learning to revolutionize the field by addressing complex problems, such as natural language understanding, speech recognition, and language translation. The authors provide insights into the latest research trends and emerging directions in language and speech processing, highlighting the contributions of deep learning algorithms and models.

In the subsequent chapters, the book delves into the theoretical foundations and mathematical principles of deep learning. It explains the underlying mechanisms of neural networks, convolutional neural networks, and recurrent neural networks, which are the building blocks of deep learning. The authors provide a comprehensive overview of the training algorithms, optimization techniques, and evaluation metrics used in deep learning, enabling readers to grasp the intricacies of this technology.

Furthermore, the book presents extensive experimental results and applications of deep learning in language and speech processing. It showcases real-world case studies and practical implementations of deep learning models, demonstrating their effectiveness in various tasks such as text classification, speech recognition, sentiment analysis, and automatic speech generation. The authors provide detailed explanations of the algorithms, models, and datasets used in these applications, enabling readers to replicate and extend the research findings.

To enhance accessibility and practicality, the book includes comprehensive references and an extensive bibliography. It serves as a valuable resource for researchers, practitioners, and students seeking to delve deeper into the realm of deep learning and its applications in language and speech processing.

In conclusion, this book offers a comprehensive and insightful exploration of the transformative power of deep learning techniques in language and speech processing. It provides a solid foundation for understanding the potential of this technology, while also addressing the challenges and limitations that arise along the way. By presenting a wealth of research-oriented articles and practical implementations, the book empowers readers to leverage deep learning to advance their work in this field and contribute to the ongoing advancements in natural language processing and speech signal processing.

Weight: 474g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030797775
Edition number: 1st ed. 2021