Robust Automatic Speech Recognition

" Robust Automatic Speech Recognition: A Bridge to Practical Applications" establishes a solid and consistent foundation for noise-robust automatic speech recognition (ASR) and provides a thorough overview of modern noise-robust techniques which have been developed over the past 30 years. The book emphasizes practical methods that are proven to be successful, also discussing those that are likely to be developed further for future applications. In addition, the pros and cons of using noise-robust ASR techniques for different applications are given, providing users with a practical guide to selecting the best methods for future applications. Connects noise-robust speech recognition methods to machine learning technologiesContains a unified, state-of-the-art survey of successful noise robust speech recognition technologiesProvides several ways to classify noise-robust speech recognition technologies into different categoriesAuthored by leading researchers at Microsoft

Produk Detail:

  • Author : Jinyu Li
  • Publisher : Academic Press
  • Pages : 250 pages
  • ISBN : 9780128023983
  • 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,Yifan Gong,Reinhold Hab-Umbach
  • Publisher : Academic Press
  • Release : 15 October 2015
GET THIS BOOKRobust Automatic Speech Recognition

" Robust Automatic Speech Recognition: A Bridge to Practical Applications" establishes a solid and consistent foundation for noise-robust automatic speech recognition (ASR) and provides a thorough overview of modern noise-robust techniques which have been developed over the past 30 years. The book emphasizes practical methods that are proven to be successful, also discussing those that are likely to be developed further for future applications. In addition, the pros and cons of using noise-robust ASR techniques for different applications are given, providing users

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

Robustness in Automatic Speech Recognition

Robustness in Automatic Speech Recognition
  • Author : Jean-Claude Junqua,Jean-Paul Haton
  • Publisher : Springer Science & Business Media
  • Release : 06 December 2012
GET THIS BOOKRobustness in Automatic Speech Recognition

Foreword Looking back the past 30 years. we have seen steady progress made in the area of speech science and technology. I still remember the excitement in the late seventies when Texas Instruments came up with a toy named "Speak-and-Spell" which was based on a VLSI chip containing the state-of-the-art linear prediction synthesizer. This caused a speech technology fever among the electronics industry. Particularly. applications of automatic speech recognition were rigorously attempt ed by many companies. some of which were start-ups

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 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

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

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 : 30 October 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

Robustness in Automatic Speech Recognition

Robustness in Automatic Speech Recognition
  • Author : Jean-Claude Junqua,Jean-Paul Haton
  • Publisher : Springer
  • Release : 24 June 1996
GET THIS BOOKRobustness in Automatic Speech Recognition

The domain of speech processing has come to the point where researchers and engineers are concerned with how speech technology can be applied to new products, and how this technology will transform our future. One important problem is to improve robustness of speech processing under adverse conditions, which is the subject of this book. Robust speech processing is a relatively new area which became a concern as technology started moving from laboratory to field applications. A method or an algorithm