Deep learning approaches for spoken and natural language processing
著者
書誌事項
Deep learning approaches for spoken and natural language processing
（Signals and communication technology）
Springer, c2021
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注記
Other editors: Amitoj Singh, Mohit Mittal, Laith Abualigah
Includes bibliographical references and index
内容説明・目次
内容説明
This book provides insights into how deep learning techniques impact language and speech processing applications. The authors discuss the promise, limits and the new challenges in deep learning. The book covers the major differences between the various applications of deep learning and the classical machine learning techniques. The main objective of the book is to present a comprehensive survey of the major applications and research oriented articles based on deep learning techniques that are focused on natural language and speech signal processing. The book is relevant to academicians, research scholars, industrial experts, scientists and post graduate students working in the field of speech signal and natural language processing and would like to add deep learning to enhance capabilities of their work.
Discusses current research challenges and future perspective about how deep learning techniques can be applied to improve NLP and speech processing applications;
Presents and escalates the research trends and future direction of language and speech processing;
Includes theoretical research, experimental results, and applications of deep learning.
目次
Introduction.- Fundamentals of Speech Perception, Production and Acquisition.- How to focus on Phonetics, Phonology and Prosody.- Analysis of Paralinguistic in Speech and Language.- Factor affecting in designing of a particular language Corpus.- Role of Deep Learning methods in Speaker and Language Identification.- Analysis of Language, Speech and Audio Signals.- Use of Deep learning approaches in Speech Coding and Enhancement.- Case studies of Speech Synthesis and Spoken Language Generation.- Processing of Speech Recognition.- Design and Development of DNN based Speech.- Recognition and Language Processing systems.- Implementation of Speech Recognition.- Visualize the Spoken Language Processing systems.- Spoken and Language Processing.- Conclusion.
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