Robust Automatic Speech Recognition

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

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  • Author : Jinyu Li
  • Publisher : Academic Press
  • Pages : 306 pages
  • ISBN : 0128026162
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKRobust Automatic Speech Recognition

Robust Automatic Speech Recognition

Robust Automatic Speech Recognition
  • Author : Jinyu Li,Li Deng,Reinhold Haeb-Umbach,Yifan Gong
  • Publisher : Academic Press
  • Release : 30 October 2015
GET THIS BOOKRobust Automatic Speech Recognition

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

Techniques for Noise Robustness in Automatic Speech Recognition

Techniques for Noise Robustness in Automatic Speech Recognition
  • Author : Tuomas Virtanen,Rita Singh,Bhiksha Raj
  • Publisher : John Wiley & Sons
  • Release : 28 November 2012
GET THIS BOOKTechniques for Noise Robustness in Automatic Speech Recognition

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

Acoustical and Environmental Robustness in Automatic Speech Recognition

Acoustical and Environmental Robustness in Automatic Speech Recognition
  • Author : A. Acero
  • Publisher : Springer Science & Business Media
  • Release : 06 December 2012
GET THIS BOOKAcoustical and Environmental Robustness in Automatic Speech Recognition

The need for automatic speech recognition systems to be robust with respect to changes in their acoustical environment has become more widely appreciated in recent years, as more systems are finding their way into practical applications. Although the issue of environmental robustness has received only a small fraction of the attention devoted to speaker independence, even speech recognition systems that are designed to be speaker independent frequently perform very poorly when they are tested using a different type of microphone

Robust Speech Recognition of Uncertain or Missing Data

Robust Speech Recognition of Uncertain or Missing Data
  • Author : Dorothea Kolossa,Reinhold Haeb-Umbach
  • Publisher : Springer Science & Business Media
  • Release : 14 July 2011
GET THIS BOOKRobust Speech Recognition of Uncertain or Missing Data

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

Robust Speech

Robust Speech
  • Author : Michael Grimm,Kristian Kroschel
  • Publisher : BoD – Books on Demand
  • Release : 01 June 2007
GET THIS BOOKRobust Speech

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

New Era for Robust Speech Recognition

New Era for Robust Speech Recognition
  • Author : Shinji Watanabe,Marc Delcroix,Florian Metze,John R. Hershey
  • Publisher : Springer
  • Release : 10 November 2017
GET THIS BOOKNew Era for Robust Speech Recognition

This book covers the state-of-the-art in deep neural-network-based methods for noise robustness in distant speech recognition applications. It provides insights and detailed descriptions of some of the new concepts and key technologies in the field, including novel architectures for speech enhancement, microphone arrays, robust features, acoustic model adaptation, training data augmentation, and training criteria. The contributed chapters also include descriptions of real-world applications, benchmark tools and datasets widely used in the field. This book is intended for researchers and practitioners

Robust Adaptation to Non-Native Accents in Automatic Speech Recognition

Robust Adaptation to Non-Native Accents in Automatic Speech Recognition
  • Author : Silke Goronzy
  • Publisher : Springer
  • Release : 01 July 2003
GET THIS BOOKRobust Adaptation to Non-Native Accents in Automatic Speech Recognition

Speech recognition technology is being increasingly employed in human-machine interfaces. A remaining problem however is the robustness of this technology to non-native accents, which still cause considerable difficulties for current systems. In this book, methods to overcome this problem are described. A speaker adaptation algorithm that is capable of adapting to the current speaker with just a few words of speaker-specific data based on the MLLR principle is developed and combined with confidence measures that focus on phone durations as

Automatic Speech and Speaker Recognition

Automatic Speech and Speaker Recognition
  • Author : Chin-Hui Lee,Frank K. Soong,Kuldip K. Paliwal
  • Publisher : Springer Science & Business Media
  • Release : 06 December 2012
GET THIS BOOKAutomatic Speech and Speaker Recognition

Research in the field of automatic speech and speaker recognition has made a number of significant advances in the last two decades, influenced by advances in signal processing, algorithms, architectures, and hardware. These advances include: the adoption of a statistical pattern recognition paradigm; the use of the hidden Markov modeling framework to characterize both the spectral and the temporal variations in the speech signal; the use of a large set of speech utterance examples from a large population of speakers

Civil, Architecture and Environmental Engineering Volume 2

Civil, Architecture and Environmental Engineering Volume 2
  • Author : Jimmy C.M. Kao,Wen-Pei Sung
  • Publisher : CRC Press
  • Release : 19 September 2017
GET THIS BOOKCivil, Architecture and Environmental Engineering Volume 2

The 2016 International Conference on Civil, Architecture and Environmental Engineering (ICCAE 2016), November 4-6, 2016, Taipei, Taiwan, is organized by China University of Technology and Taiwan Society of Construction Engineers, aimed to bring together professors, researchers, scholars and industrial pioneers from all over the world. ICCAE 2016 is the premier forum for the presentation and exchange of experience, progress and research results in the field of theoretical and industrial experience. The conference consists of contributions promoting the exchange of ideas between researchers and educators

Noise Reduction in Speech Applications

Noise Reduction in Speech Applications
  • Author : Gillian M. Davis
  • Publisher : CRC Press
  • Release : 03 October 2018
GET THIS BOOKNoise Reduction in Speech Applications

Noise and distortion that degrade the quality of speech signals can come from any number of sources. The technology and techniques for dealing with noise are almost as numerous, but it is only recently, with the development of inexpensive digital signal processing hardware, that the implementation of the technology has become practical. Noise Reduction in Speech Applications provides a comprehensive introduction to modern techniques for removing or reducing background noise from a range of speech-related applications. Self-contained, it starts with

Statistical Language and Speech Processing

Statistical Language and Speech Processing
  • Author : Thierry Dutoit,Carlos Martín-Vide,Gueorgui Pironkov
  • Publisher : Springer
  • Release : 08 October 2018
GET THIS BOOKStatistical Language and Speech Processing

This book constitutes the proceedings of the 6th International Conference on Statistical Language and Speech Processing, SLSP 2018, held in Mons, Belgium, in October 2018. The 15 full papers presented in this volume were carefully reviewed and selected from 40 submissions. They were organized in topical sections named: speech synthesis and spoken language generation; speech recognition and post-processing; natural language processing and understanding; and text processing and analysis.

Automatic Speech Recognition

Automatic Speech Recognition
  • Author : Dong Yu,Li Deng
  • Publisher : Springer
  • Release : 11 November 2014
GET THIS BOOKAutomatic Speech Recognition

This book provides a comprehensive overview of the recent advancement in the field of automatic speech recognition with a focus on deep learning models including deep neural networks and many of their variants. This is the first automatic speech recognition book dedicated to the deep learning approach. In addition to the rigorous mathematical treatment of the subject, the book also presents insights and theoretical foundation of a series of highly successful deep learning models.

Audio Source Separation and Speech Enhancement

Audio Source Separation and Speech Enhancement
  • Author : Emmanuel Vincent,Tuomas Virtanen,Sharon Gannot
  • Publisher : John Wiley & Sons
  • Release : 24 July 2018
GET THIS BOOKAudio Source Separation and Speech Enhancement

Learn the technology behind hearing aids, Siri, and Echo Audio source separation and speech enhancement aim to extract one or more source signals of interest from an audio recording involving several sound sources. These technologies are among the most studied in audio signal processing today and bear a critical role in the success of hearing aids, hands-free phones, voice command and other noise-robust audio analysis systems, and music post-production software. Research on this topic has followed three convergent paths, starting