Over the last 20 years, approaches to designing speech and language processing algorithms have moved from methods based on linguistics and speech science to data-driven pattern recognition techniques. These techniques have been the focus of intense, fast-moving research and have contributed to significant advances in this field. Pattern Reco
Pattern Recognition by Humans and Machines, Volume 1: Speech Perception covers perception from the perspectives of cognitive psychology, artificial intelligence, and brain theory. The book discusses on the research, theory, and the principal issues of speech perception; the auditory and phonetic coding of speech; and the role of the lexicon in speech perception. The text also describes the role of attention and active processing in speech perception; the suprasegmental in very large vocabulary word recognition; and the adaptive self-organization of serial order in behavior. The cognitive science and the study of cognition and language are also considered. Psychologists will find the book invaluable.
During the past fifteen years there has been a considerable growth of interest in problems of pattern recognition. Contributions to the blossom of this area have come from many disciplines, including statistics, psychology, linguistics, computer science, biology, taxonomy, switching theory, communication theory, control theory, and operations research. Many different approaches have been proposed and a number of books have been published. Most books published so far deal with the decision-theoretic (or statistical) approach or the syntactic (or linguistic) approach. Since the area of pattern recognition is still far from its maturity, many new research results, both in theory and in applications, are continuously produced. The purpose of this monograph is to provide a concise summary of the major recent developments in pattern recognition. The five main chapters (Chapter 2-6) in this book can be divided into two parts. The first three chapters concern primarily with basic techniques in pattern recognition. They include statistical techniques, clustering analysis and syntactic techniques. The last two chapters deal with applications; namely, picture recognition, and speech recognition and understanding. Each chapter is written by one or two distinguished experts on that subject. The editor has not attempted to impose upon the contributors to this volume a uniform notation and terminol ogy, since such notation and terminology does not as yet exist in pattern recognition.
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.
This book constitutes the refereed proceedings of the 8th Iberoamerican Congress on Pattern Recognition, CIARP 2003, held in Havana, Cuba, in November 2003. The 82 revised full papers presented together with two invited papers were carefully reviewed and selected from 140 submissions. All current issues in pattern recognition, image processing, and computer vision are addressed as well as applications in domains like robotics, health, entertainment, space exploration, telecommunications, speech processing, data analysis, document recognition, etc.
This monograph is intended to cover several major applications of pattern recognition. After a brief introduction to pattern recognition in Chapter 1, the two major approaches, statistical approach and syntactic approach, are reviewed in Chapter 2, and 3, respectively. Other topics include the application of pattern recognition to seismic wave interpretation, to system reliability problems, to medical data analysis, as well as character and speech recognition.
What Is Speech Recognition Computer science and computational linguistics have spawned a subfield known as speech recognition, which is an interdisciplinary field that focuses on the development of methodologies and technologies that enable computers to recognize and translate spoken language into text. The primary advantage of this is that the text can then be searched. Automatic speech recognition, sometimes abbreviated as ASR, is another name for it, as is computer speech recognition and voice to text (STT). 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: Speech synthesis Chapter 6: Vector quantization Chapter 7: Pattern recognition Chapter 8: Lawrence Rabiner Chapter 9: Recurrent neural network Chapter 10: Julius (software) Chapter 11: Long short-term memory Chapter 12: Time delay neural network Chapter 13: Types of artificial neural networks Chapter 14: Deep learning Chapter 15: Nelson Morgan Chapter 16: Sinsy Chapter 17: Outline of machine learning Chapter 18: Steve Young (academic) Chapter 19: Tony Robinson (speech recognition) Chapter 20: Voice computing Chapter 21: Joseph Keshet (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.
This book demonstrates the efficiency of the C++ programming language in the realm of pattern recognition and pattern analysis. It introduces the basics of software engineering, image and speech processing, als well as fundamental mathematical tools for pattern recognition. Step by step the C++ programming language is discribed. Each step is illustrated by examples based on challenging problems in image und speech processing. Particular emphasis is put on object-oriented programming and the implementation of efficient algorithms. The book proposes a general class hierarchy for image segmentation. The essential parts of an implementation are presented. An object-oriented system for speech classification based on stochastic models is described.
Spoken language understanding (SLU) is an emerging field in between speech and language processing, investigating human/ machine and human/ human communication by leveraging technologies from signal processing, pattern recognition, machine learning and artificial intelligence. SLU systems are designed to extract the meaning from speech utterances and its applications are vast, from voice search in mobile devices to meeting summarization, attracting interest from both commercial and academic sectors. Both human/machine and human/human communications can benefit from the application of SLU, using differing tasks and approaches to better understand and utilize such communications. This book covers the state-of-the-art approaches for the most popular SLU tasks with chapters written by well-known researchers in the respective fields. Key features include: Presents a fully integrated view of the two distinct disciplines of speech processing and language processing for SLU tasks. Defines what is possible today for SLU as an enabling technology for enterprise (e.g., customer care centers or company meetings), and consumer (e.g., entertainment, mobile, car, robot, or smart environments) applications and outlines the key research areas. Provides a unique source of distilled information on methods for computer modeling of semantic information in human/machine and human/human conversations. This book can be successfully used for graduate courses in electronics engineering, computer science or computational linguistics. Moreover, technologists interested in processing spoken communications will find it a useful source of collated information of the topic drawn from the two distinct disciplines of speech processing and language processing under the new area of SLU.
