Robust Automatic Speech Recognition

Robust Automatic Speech Recognition

Author: Jinyu Li

Publisher: Academic Press

Published: 2015-10-30

Total Pages: 308

ISBN-13: 0128026162

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Robust Automatic Speech Recognition: A Bridge to Practical Applications establishes a solid foundation for automatic speech recognition that is robust against acoustic environmental distortion. It provides a thorough overview of classical and modern noise-and reverberation robust techniques that have been developed over the past thirty years, with an emphasis on practical methods that have been proven to be successful and which are likely to be further developed for future applications.The strengths and weaknesses of robustness-enhancing speech recognition techniques are carefully analyzed. The book covers noise-robust techniques designed for acoustic models which are based on both Gaussian mixture models and deep neural networks. In addition, a guide to selecting the best methods for practical applications is provided.The reader will: - Gain a unified, deep and systematic understanding of the state-of-the-art technologies for robust speech recognition - Learn the links and relationship between alternative technologies for robust speech recognition - Be able to use the technology analysis and categorization detailed in the book to guide future technology development - Be able to develop new noise-robust methods in the current era of deep learning for acoustic modeling in speech recognition - The first book that provides a comprehensive review on noise and reverberation robust speech recognition methods in the era of deep neural networks - Connects robust speech recognition techniques to machine learning paradigms with rigorous mathematical treatment - Provides elegant and structural ways to categorize and analyze noise-robust speech recognition techniques - Written by leading researchers who have been actively working on the subject matter in both industrial and academic organizations for many years


Techniques for Noise Robustness in Automatic Speech Recognition

Techniques for Noise Robustness in Automatic Speech Recognition

Author: Tuomas Virtanen

Publisher: John Wiley & Sons

Published: 2012-11-28

Total Pages: 514

ISBN-13: 1119970881

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Automatic speech recognition (ASR) systems are finding increasing use in everyday life. Many of the commonplace environments where the systems are used are noisy, for example users calling up a voice search system from a busy cafeteria or a street. This can result in degraded speech recordings and adversely affect the performance of speech recognition systems. As the use of ASR systems increases, knowledge of the state-of-the-art in techniques to deal with such problems becomes critical to system and application engineers and researchers who work with or on ASR technologies. This book presents a comprehensive survey of the state-of-the-art in techniques used to improve the robustness of speech recognition systems to these degrading external influences. Key features: Reviews all the main noise robust ASR approaches, including signal separation, voice activity detection, robust feature extraction, model compensation and adaptation, missing data techniques and recognition of reverberant speech. Acts as a timely exposition of the topic in light of more widespread use in the future of ASR technology in challenging environments. Addresses robustness issues and signal degradation which are both key requirements for practitioners of ASR. Includes contributions from top ASR researchers from leading research units in the field


Robust Speech

Robust Speech

Author: Michael Grimm

Publisher: BoD – Books on Demand

Published: 2007-06-01

Total Pages: 471

ISBN-13: 3902613084

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This book on Robust Speech Recognition and Understanding brings together many different aspects of the current research on automatic speech recognition and language understanding. The first four chapters address the task of voice activity detection which is considered an important issue for all speech recognition systems. The next chapters give several extensions to state-of-the-art HMM methods. Furthermore, a number of chapters particularly address the task of robust ASR under noisy conditions. Two chapters on the automatic recognition of a speaker's emotional state highlight the importance of natural speech understanding and interpretation in voice-driven systems. The last chapters of the book address the application of conversational systems on robots, as well as the autonomous acquisition of vocalization skills.


Recent Advances in Robust Speech Recognition Technology

Recent Advances in Robust Speech Recognition Technology

Author: Javier Ramirez

Publisher: Bentham Science

Published: 2011

Total Pages: 223

ISBN-13: 1608051722

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"This E-book is a collection of articles that describe advances in speech recognition technology. Robustness in speech recognition refers to the need to maintain high speech recognition accuracy even when the quality of the input speech is degraded, or whe"


Modern Speech Recognition

Modern Speech Recognition

Author: S. Ramakrishnan

Publisher:

Published: 2012

Total Pages: 340

ISBN-13: 9789535156680

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This book focuses primarily on speech recognition and the related tasks such as speech enhancement and modeling. This book comprises 3 sections and thirteen chapters written by eminent researchers from USA, Brazil, Australia, Saudi Arabia, Japan, Ireland, Taiwan, Mexico, Slovakia and India. Section 1 on speech recognition consists of seven chapters. Sections 2 and 3 on speech enhancement and speech modeling have three chapters each respectively to supplement section 1. We sincerely believe that thorough reading of these thirteen chapters will provide comprehensive knowledge on modern speech recognition approaches to the readers.


Robust Speech Recognition of Uncertain or Missing Data

Robust Speech Recognition of Uncertain or Missing Data

Author: Dorothea Kolossa

Publisher: Springer Science & Business Media

Published: 2011-07-14

Total Pages: 387

ISBN-13: 3642213170

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Automatic speech recognition suffers from a lack of robustness with respect to noise, reverberation and interfering speech. The growing field of speech recognition in the presence of missing or uncertain input data seeks to ameliorate those problems by using not only a preprocessed speech signal but also an estimate of its reliability to selectively focus on those segments and features that are most reliable for recognition. This book presents the state of the art in recognition in the presence of uncertainty, offering examples that utilize uncertainty information for noise robustness, reverberation robustness, simultaneous recognition of multiple speech signals, and audiovisual speech recognition. The book is appropriate for scientists and researchers in the field of speech recognition who will find an overview of the state of the art in robust speech recognition, professionals working in speech recognition who will find strategies for improving recognition results in various conditions of mismatch, and lecturers of advanced courses on speech processing or speech recognition who will find a reference and a comprehensive introduction to the field. The book assumes an understanding of the fundamentals of speech recognition using Hidden Markov Models.


The Application of Hidden Markov Models in Speech Recognition

The Application of Hidden Markov Models in Speech Recognition

Author: Mark Gales

Publisher: Now Publishers Inc

Published: 2008

Total Pages: 125

ISBN-13: 1601981201

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The Application of Hidden Markov Models in Speech Recognition presents the core architecture of a HMM-based LVCSR system and proceeds to describe the various refinements which are needed to achieve state-of-the-art performance.


Robust Speech Recognition in Embedded Systems and PC Applications

Robust Speech Recognition in Embedded Systems and PC Applications

Author: Jean-Claude Junqua

Publisher: Springer Science & Business Media

Published: 2006-04-18

Total Pages: 193

ISBN-13: 0306470276

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Robust Speech Recognition in Embedded Systems and PC Applications provides a link between the technology and the application worlds. As speech recognition technology is now good enough for a number of applications and the core technology is well established around hidden Markov models many of the differences between systems found in the field are related to implementation variants. We distinguish between embedded systems and PC-based applications. Embedded applications are usually cost sensitive and require very simple and optimized methods to be viable. Robust Speech Recognition in Embedded Systems and PC Applications reviews the problems of robust speech recognition, summarizes the current state of the art of robust speech recognition while providing some perspectives, and goes over the complementary technologies that are necessary to build an application, such as dialog and user interface technologies. Robust Speech Recognition in Embedded Systems and PC Applications is divided into five chapters. The first one reviews the main difficulties encountered in automatic speech recognition when the type of communication is unknown. The second chapter focuses on environment-independent/adaptive speech recognition approaches and on the mainstream methods applicable to noise robust speech recognition. The third chapter discusses several critical technologies that contribute to making an application usable. It also provides some design recommendations on how to design prompts, generate user feedback and develop speech user interfaces. The fourth chapter reviews several techniques that are particularly useful for embedded systems or to decrease computational complexity. It also presents some case studies for embedded applications and PC-based systems. Finally, the fifth chapter provides a future outlook for robust speech recognition, emphasizing the areas that the author sees as the most promising for the future. Robust Speech Recognition in Embedded Systems and PC Applications serves as a valuable reference and although not intended as a formal University textbook, contains some material that can be used for a course at the graduate or undergraduate level. It is a good complement for the book entitled Robustness in Automatic Speech Recognition: Fundamentals and Applications co-authored by the same author.


Speech Processing in the Auditory System

Speech Processing in the Auditory System

Author: Steven Greenberg

Publisher: Springer Science & Business Media

Published: 2006-05-09

Total Pages: 487

ISBN-13: 0387215751

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Although speech is the primary behavioral medium by which humans communicate, its auditory basis is poorly understood, having profound implications on efforts to ameliorate the behavioral consequences of hearing impairment and on the development of robust algorithms for computer speech recognition. In this volume, the authors provide an up-to-date synthesis of recent research in the area of speech processing in the auditory system, bringing together a diverse range of scientists to present the subject from an interdisciplinary perspective. Of particular concern is the ability to understand speech in uncertain, potentially adverse acoustic environments, currently the bane of both hearing aid and speech recognition technology. There is increasing evidence that the perceptual stability characteristic of speech understanding is due, at least in part, to elegant transformations of the acoustic signal performed by auditory mechanisms. As a comprehensive review of speech's auditory basis, this book will interest physiologists, anatomists, psychologists, phoneticians, computer scientists, biomedical and electrical engineers, and clinicians.


Signal Analysis and Prediction

Signal Analysis and Prediction

Author: Ales Prochazka

Publisher: Springer Science & Business Media

Published: 1998-12-23

Total Pages: 536

ISBN-13: 9780817640422

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Methods of signal analysis represent a broad research topic with applications in many disciplines, including engineering, technology, biomedicine, seismography, eco nometrics, and many others based upon the processing of observed variables. Even though these applications are widely different, the mathematical background be hind them is similar and includes the use of the discrete Fourier transform and z-transform for signal analysis, and both linear and non-linear methods for signal identification, modelling, prediction, segmentation, and classification. These meth ods are in many cases closely related to optimization problems, statistical methods, and artificial neural networks. This book incorporates a collection of research papers based upon selected contri butions presented at the First European Conference on Signal Analysis and Predic tion (ECSAP-97) in Prague, Czech Republic, held June 24-27, 1997 at the Strahov Monastery. Even though the Conference was intended as a European Conference, at first initiated by the European Association for Signal Processing (EURASIP), it was very gratifying that it also drew significant support from other important scientific societies, including the lEE, Signal Processing Society of IEEE, and the Acoustical Society of America. The organizing committee was pleased that the re sponse from the academic community to participate at this Conference was very large; 128 summaries written by 242 authors from 36 countries were received. In addition, the Conference qualified under the Continuing Professional Development Scheme to provide PD units for participants and contributors.