Techniques for Noise Robustness in Automatic Speech Recognition PDF Download
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Author: Tuomas Virtanen
Publisher: John Wiley & Sons
ISBN: 1119970881
Category : Technology & Engineering
Languages : en
Pages : 514
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Book Description
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
Author: Tuomas Virtanen
Publisher: John Wiley & Sons
ISBN: 1119970881
Category : Technology & Engineering
Languages : en
Pages : 514
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Book Description
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
Author: Jinyu Li
Publisher: Academic Press
ISBN: 0128026162
Category : Technology & Engineering
Languages : en
Pages : 308
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Book Description
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
Author: Shinji Watanabe
Publisher: Springer
ISBN: 331964680X
Category : Computers
Languages : en
Pages : 433
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Book Description
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 working in the field of speech processing and recognition who are interested in the latest deep learning techniques for noise robustness. It will also be of interest to graduate students in electrical engineering or computer science, who will find it a useful guide to this field of research.
Author: Michael Grimm
Publisher: BoD – Books on Demand
ISBN: 3902613084
Category : Computers
Languages : en
Pages : 471
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Book Description
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.
Author: Matthias Woelfel
Publisher: John Wiley & Sons
ISBN: 0470714077
Category : Technology & Engineering
Languages : en
Pages : 600
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Book Description
A complete overview of distant automatic speech recognition The performance of conventional Automatic Speech Recognition (ASR) systems degrades dramatically as soon as the microphone is moved away from the mouth of the speaker. This is due to a broad variety of effects such as background noise, overlapping speech from other speakers, and reverberation. While traditional ASR systems underperform for speech captured with far-field sensors, there are a number of novel techniques within the recognition system as well as techniques developed in other areas of signal processing that can mitigate the deleterious effects of noise and reverberation, as well as separating speech from overlapping speakers. Distant Speech Recognitionpresents a contemporary and comprehensive description of both theoretic abstraction and practical issues inherent in the distant ASR problem. Key Features: Covers the entire topic of distant ASR and offers practical solutions to overcome the problems related to it Provides documentation and sample scripts to enable readers to construct state-of-the-art distant speech recognition systems Gives relevant background information in acoustics and filter techniques, Explains the extraction and enhancement of classification relevant speech features Describes maximum likelihood as well as discriminative parameter estimation, and maximum likelihood normalization techniques Discusses the use of multi-microphone configurations for speaker tracking and channel combination Presents several applications of the methods and technologies described in this book Accompanying website with open source software and tools to construct state-of-the-art distant speech recognition systems This reference will be an invaluable resource for researchers, developers, engineers and other professionals, as well as advanced students in speech technology, signal processing, acoustics, statistics and artificial intelligence fields.
Author: Joseph Keshet
Publisher: John Wiley & Sons
ISBN: 9780470742037
Category : Technology & Engineering
Languages : en
Pages : 268
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Book Description
This book discusses large margin and kernel methods for speech and speaker recognition Speech and Speaker Recognition: Large Margin and Kernel Methods is a collation of research in the recent advances in large margin and kernel methods, as applied to the field of speech and speaker recognition. It presents theoretical and practical foundations of these methods, from support vector machines to large margin methods for structured learning. It also provides examples of large margin based acoustic modelling for continuous speech recognizers, where the grounds for practical large margin sequence learning are set. Large margin methods for discriminative language modelling and text independent speaker verification are also addressed in this book. Key Features: Provides an up-to-date snapshot of the current state of research in this field Covers important aspects of extending the binary support vector machine to speech and speaker recognition applications Discusses large margin and kernel method algorithms for sequence prediction required for acoustic modeling Reviews past and present work on discriminative training of language models, and describes different large margin algorithms for the application of part-of-speech tagging Surveys recent work on the use of kernel approaches to text-independent speaker verification, and introduces the main concepts and algorithms Surveys recent work on kernel approaches to learning a similarity matrix from data This book will be of interest to researchers, practitioners, engineers, and scientists in speech processing and machine learning fields.
Author: Mark Gales
Publisher: Now Publishers Inc
ISBN: 1601981201
Category : Automatic speech recognition
Languages : en
Pages : 125
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Book Description
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.
Author: Michael Brandstein
Publisher: Springer Science & Business Media
ISBN: 3662046199
Category : Technology & Engineering
Languages : en
Pages : 401
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Book Description
This is the first book to provide a single complete reference on microphone arrays. Top researchers in this field contributed articles documenting the current state of the art in microphone array research, development and technological application.
Author: Nilanjan Dey
Publisher: Academic Press
ISBN: 0128181303
Category : Technology & Engineering
Languages : en
Pages : 210
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Book Description
Intelligent Speech Signal Processing investigates the utilization of speech analytics across several systems and real-world activities, including sharing data analytics, creating collaboration networks between several participants, and implementing video-conferencing in different application areas. Chapters focus on the latest applications of speech data analysis and management tools across different recording systems. The book emphasizes the multidisciplinary nature of the field, presenting different applications and challenges with extensive studies on the design, development and management of intelligent systems, neural networks and related machine learning techniques for speech signal processing.
Author: Shoji Makino
Publisher: Springer Science & Business Media
ISBN: 9783540240396
Category : Hearing
Languages : en
Pages : 432
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Book Description
We live in a noisy world! In all applications (telecommunications, hands-free communications, recording, human-machine interfaces, etc.) that require at least one microphone, the signal of interest is usually contaminated by noise and reverberation. As a result, the microphone signal has to be "cleaned" with digital signal processing tools before it is played out, transmitted, or stored. This book is about speech enhancement. Different well-known and state-of-the-art methods for noise reduction, with one or multiple microphones, are discussed. By speech enhancement, we mean not only noise reduction but also dereverberation and separation of independent signals. These topics are also covered in this book. However, the general emphasis is on noise reduction because of the large number of applications that can benefit from this technology. The goal of this book is to provide a strong reference for researchers, engineers, and graduate students who are interested in the problem of signal and speech enhancement. To do so, we invited well-known experts to contribute chapters covering the state of the art in this focused field. TOC:Introduction.- Study of the Wiener Filter for Noise Reduction.- Statistical Methods for the Enhancement of Noisy Speech.- Single- und Multi-Microphone Spectral Amplitude Estimation Using a Super-Gaussian Speech Model.- From Volatility Modeling of Financial Time-Series to Stochastic Modeling and Enhancement of Speech Signals.- Single-Microphone Noise Suppression for 3G Handsets Based on Weighted Noise Estimation.- Signal Subspace Techniques for Speech Enhancement.- Speech Enhancement: Application of the Kalman Filter in the Estimate-Maximize (EM) Framework.- Speech Distortion Weighted Multichannel Wiener Filtering Techniques for Noise Reduction.- Adpative Microphone Arrays Employing Spatial Quadratic Soft Constraints and Spectral Shaping.- Single-Microphone Blind Dereverberation.- Separation and Dereverberation of Speech Signals with Multiple Microphones.- Frequency-Domain Blind Source Separation.- Subband Based Blind Source Separation.- Real-Time Blind Source Separation for Moving Speech Signals.- Separation of Speech by Computational Auditory Scene Analysis
