Speech Processing, Recognition and Artificial Neural Networks contains papers from leading researchers and selected students, discussing the experiments, theories and perspectives of acoustic phonetics as well as the latest techniques in the field of spe ech science and technology. Topics covered in this book include; Fundamentals of Speech Analysis and Perceptron; Speech Processing; Stochastic Models for Speech; Auditory and Neural Network Models for Speech; Task-Oriented Applications of Automatic Speech Recognition and Synthesis.

A wide range of fields of study support speech research. They cover many fields like for instance phonetics, linguistics, psychology, cognitive science, sonics, information engineering (information theory, pattern recognition, artificial intelligence), and it is an extremely difficult job to carry all of these in one body.The first half of this book gives detailed descriptions of engineering applications, that is the speech, hearing and perception mechanisms that form the basis for automatic synthesis and recognition of speech. The second half of this book gives a detailed explanation of speech synthesis and recognition based on a collective physiological approach, that is the artificial neural networks which imitate human neural networks and have once again been bathed in attention lately. The characteristics of this book are that, along with having engineers and technicians as its main targets, it explains engineering models based on speech science.

Text-to-speech (TTS) aims to synthesize intelligible and natural speech based on the given text. It is a hot topic in language, speech, and machine learning research and has broad applications in industry. This book introduces neural network-based TTS in the era of deep learning, aiming to provide a good understanding of neural TTS, current research and applications, and the future research trend. This book first introduces the history of TTS technologies and overviews neural TTS, and provides preliminary knowledge on language and speech processing, neural networks and deep learning, and deep generative models. It then introduces neural TTS from the perspective of key components (text analyses, acoustic models, vocoders, and end-to-end models) and advanced topics (expressive and controllable, robust, model-efficient, and data-efficient TTS). It also points some future research directions and collects some resources related to TTS. This book is the first to introduce neural TTS in a comprehensive and easy-to-understand way and can serve both academic researchers and industry practitioners working on TTS.

Intelligent Speech Signal Processing investigates the utilization of speech analytics across several systems and real-world activities, including sharing data analytics related information, creating collaboration networks between several participants, and implementing video-conferencing in different application areas. It provides a forum for readers to discover the characteristics of intelligent speech signal processing systems across different domains. Chapters focus on the latest applications of speech data analysis and management tools across different recording systems. The book emphasizes the multi-disciplinary nature of the field, presenting different applications and challenges with extensive studies on the design, implementation, development, and management of intelligent systems, neural networks, and related machine learning techniques for speech signal processing. Highlights different data analytics techniques in speech signal processing, including machine learning, and data mining Illustrates different applications and challenges across the design, implementation, and management of intelligent systems and neural networks techniques for speech signal processing Includes coverage of biomodal speech recognition, voice activity detection, spoken language and speech disorder identification, automatic speech to speech summarization, and convolutional neural networks

Sequence recognition is a crucial element in many applications in the fields of speech analysis, control, and modeling. This book applies the techniques of neural networks and hidden Markov models to the problems of sequence recognition, and as such will prove valuable to researchers and graduate students alike.

What Is Speech Recognition Computer science and computational linguistics include a subfield called speech recognition that focuses on the development of approaches and technologies that enable computers to recognize spoken language and translate it into text. Speech recognition is an interdisciplinary subfield of computer science. It is also known as computer speech recognition (CSR) and speech to text (STT). Another name for it is automatic speech recognition (ASR). The domains of computer science, linguistics, and computer engineering are all represented in its incorporation of knowledge and study. Speech synthesis is the process of doing things backwards. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Speech recognition Chapter 2: Computational linguistics Chapter 3: Natural language processing Chapter 4: Speech processing Chapter 5: Pattern recognition Chapter 6: Language model Chapter 7: Deep learning Chapter 8: Recurrent neural network Chapter 9: Long short-term memory Chapter 10: Voice computing (II) Answering the public top questions about speech recognition. (III) Real world examples for the usage of speech recognition in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of speech recognition' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of speech recognition.