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| Author | : Hans-Günter Hirsch |
| Publisher | : |
| Total Pages | : 65 |
| Release | : 2008 |
| Genre | : |
| ISBN | : 9783832272265 |
| Author | : Tuomas Virtanen |
| Publisher | : John Wiley & Sons |
| Total Pages | : 514 |
| Release | : 2012-09-19 |
| Genre | : Technology & Engineering |
| ISBN | : 1118392663 |
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 | : Jean-Claude Junqua |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 457 |
| Release | : 2012-12-06 |
| Genre | : Technology & Engineering |
| ISBN | : 1461312973 |
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 founded just for this purpose. Unfortunately. it did not take long before they realized that automatic speech rec ognition technology was not mature enough to satisfy the need of customers. The fever gradually faded away. In the meantime. constant efforts have been made by many researchers and engi neers to improve the automatic speech recognition technology. Hardware capabilities have advanced impressively since that time. In the past few years. we have been witnessing and experiencing the advent of the "Information Revolution." What might be called the second surge of interest to com mercialize speech technology as a natural interface for man-machine communication began in much better shape than the first one. With computers much more powerful and faster. many applications look realistic this time. However. there are still tremendous practical issues to be overcome in order for speech to be truly the most natural interface between humans and machines.
| Author | : Jinyu Li |
| Publisher | : Academic Press |
| Total Pages | : 308 |
| Release | : 2015-10-30 |
| Genre | : Technology & Engineering |
| ISBN | : 0128026162 |
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 | : Dorothea Kolossa |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 387 |
| Release | : 2011-07-14 |
| Genre | : Technology & Engineering |
| ISBN | : 3642213170 |
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.
| Author | : Chin-Hui Lee |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 524 |
| Release | : 2012-12-06 |
| Genre | : Technology & Engineering |
| ISBN | : 1461313678 |
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 to train the hidden Markov models of some fundamental speech units; the organization of speech and language knowledge sources into a structural finite state network; and the use of dynamic, programming based heuristic search methods to find the best word sequence in the lexical network corresponding to the spoken utterance. Automatic Speech and Speaker Recognition: Advanced Topics groups together in a single volume a number of important topics on speech and speaker recognition, topics which are of fundamental importance, but not yet covered in detail in existing textbooks. Although no explicit partition is given, the book is divided into five parts: Chapters 1-2 are devoted to technology overviews; Chapters 3-12 discuss acoustic modeling of fundamental speech units and lexical modeling of words and pronunciations; Chapters 13-15 address the issues related to flexibility and robustness; Chapter 16-18 concern the theoretical and practical issues of search; Chapters 19-20 give two examples of algorithm and implementational aspects for recognition system realization. Audience: A reference book for speech researchers and graduate students interested in pursuing potential research on the topic. May also be used as a text for advanced courses on the subject.
| Author | : Matthias Woelfel |
| Publisher | : John Wiley & Sons |
| Total Pages | : 600 |
| Release | : 2009-04-20 |
| Genre | : Technology & Engineering |
| ISBN | : 0470714077 |
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 | : Florian Müller |
| Publisher | : Logos Verlag Berlin GmbH |
| Total Pages | : 247 |
| Release | : 2013 |
| Genre | : Computers |
| ISBN | : 3832533192 |
Automatic speech recognition systems have to handle various kinds of variabilities sufficiently well in order to achieve high recognition rates in practice. One of the variabilities that has a major impact on the performance is the vocal tract length of the speakers. Normalization of the features and adaptation of the acoustic models are commonly used methods in speech recognition systems. In contrast to that, a third approach follows the idea of extracting features with transforms that are invariant to vocal tract lengths changes. This work presents several approaches for extracting invariant features for automatic speech recognition systems. The robustness of these features under various training-test conditions is evaluated and it is described how the robustness of the features to noise can be increased. Furthermore, it is shown how the spectral effects due to different vocal tract lengths can be estimated with a registration method and how this can be used for speaker normalization.
| Author | : Marc René Schädler |
| Publisher | : |
| Total Pages | : |
| Release | : 2016 |
| Genre | : |
| ISBN | : 9783814223339 |
Automatic speech recognition (ASR) systems still do not perform as well as human listeners under realistic conditions. The unmatched ability of humans to understand speech in most difficult acoustic conditions originates from the superior properties of their auditory system. The aim of this thesis is to improve the recognition performance of ASR systems in difficult acoustic conditions by carefully integrating auditory signal processing strategies. To this end, the physiologically inspired extraction of spectro-temporal modulation patterns was successfully integrated into the front-end of a standard ASR system. Furhter the joint spectro-temporal processing could be separated into independent temporal and spectral processes. To investigate the reason for the remaining "man-maschine-gap" in recognition performance, a range of critical auditory discrimination tasks were performed using ASR systems. The comparison with empirical data showed the the seperate spectro-temporal modulation front-end provides a suitable auditory model and revealed the importance of across-frequency processing in speech recognition.
| Author | : Michael Grimm |
| Publisher | : BoD – Books on Demand |
| Total Pages | : 471 |
| Release | : 2007-06-01 |
| Genre | : Computers |
| ISBN | : 3902613084 |
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.
