Robust Automatic Speech Recognition

Robust Automatic Speech Recognition
Author: Jinyu Li,Li Deng,Reinhold Haeb-Umbach,Yifan Gong
Publsiher: Academic Press
Total Pages: 306
Release: 2015-10-30
ISBN 10: 0128026162
ISBN 13: 9780128026168
Language: EN, FR, DE, ES & NL

Robust Automatic Speech Recognition Book Review:

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,Rita Singh,Bhiksha Raj
Publsiher: John Wiley & Sons
Total Pages: 520
Release: 2012-09-19
ISBN 10: 1118392663
ISBN 13: 9781118392669
Language: EN, FR, DE, ES & NL

Techniques for Noise Robustness in Automatic Speech Recognition Book Review:

Automatic speech recognition (ASR) systems are findingincreasing use in everyday life. Many of the commonplaceenvironments where the systems are used are noisy, for exampleusers calling up a voice search system from a busy cafeteria or astreet. This can result in degraded speech recordings and adverselyaffect the performance of speech recognition systems. As theuse of ASR systems increases, knowledge of the state-of-the-art intechniques to deal with such problems becomes critical to systemand application engineers and researchers who work with or on ASRtechnologies. This book presents a comprehensive survey of thestate-of-the-art in techniques used to improve the robustness ofspeech recognition systems to these degrading externalinfluences. Key features: Reviews all the main noise robust ASR approaches, includingsignal separation, voice activity detection, robust featureextraction, model compensation and adaptation, missing datatechniques and recognition of reverberant speech. Acts as a timely exposition of the topic in light of morewidespread use in the future of ASR technology in challengingenvironments. Addresses robustness issues and signal degradation which areboth key requirements for practitioners of ASR. Includes contributions from top ASR researchers from leadingresearch units in the field

Robustness in Automatic Speech Recognition

Robustness in Automatic Speech Recognition
Author: Jean-Claude Junqua,Jean-Paul Haton
Publsiher: Springer Science & Business Media
Total Pages: 440
Release: 2012-12-06
ISBN 10: 1461312973
ISBN 13: 9781461312970
Language: EN, FR, DE, ES & NL

Robustness in Automatic Speech Recognition Book Review:

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.

Acoustical and Environmental Robustness in Automatic Speech Recognition

Acoustical and Environmental Robustness in Automatic Speech Recognition
Author: Alex Acero
Publsiher: Springer Science & Business Media
Total Pages: 186
Release: 1992-11-30
ISBN 10: 9780792392842
ISBN 13: 0792392841
Language: EN, FR, DE, ES & NL

Acoustical and Environmental Robustness in Automatic Speech Recognition Book Review:

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 or acoustical environment from the one with which they were trained. The use of microphones other than a "close talking" headset also tends to severely degrade speech recognition -performance. Even in relatively quiet office environments, speech is degraded by additive noise from fans, slamming doors, and other conversations, as well as by the effects of unknown linear filtering arising reverberation from surface reflections in a room, or spectral shaping by microphones or the vocal tracts of individual speakers. Speech-recognition systems designed for long-distance telephone lines, or applications deployed in more adverse acoustical environments such as motor vehicles, factory floors, oroutdoors demand far greaterdegrees ofenvironmental robustness. There are several different ways of building acoustical robustness into speech recognition systems. Arrays of microphones can be used to develop a directionally-sensitive system that resists intelference from competing talkers and other noise sources that are spatially separated from the source of the desired speech signal.

Robust Automatic Speech Recognition Employing Phoneme dependent Multi environment Enhanced Models Based Linear Normalization

Robust Automatic Speech Recognition Employing Phoneme dependent Multi environment Enhanced Models Based Linear Normalization
Author: Igmar Hernández Ochoa
Publsiher: Unknown
Total Pages: 329
Release: 2006
ISBN 10:
ISBN 13: OCLC:970545486
Language: EN, FR, DE, ES & NL

Robust Automatic Speech Recognition Employing Phoneme dependent Multi environment Enhanced Models Based Linear Normalization Book Review:

This work shows a robust normalization technique by cascading a speech enhance-ment method followed by a feature vector normalization algorithm. An efficient scheme used to provide speech enhancement is the Spectral Subtraction algorithm, which reduces the effect of additive noise by performing a subtraction of noise spectrum estimate over the complete speech spectrum. On the other hand, a new and promising technique known as PD-MEMLIN (Phoneme-Dependent Multi-Enviroment Models based Linear Normalization) has also shown to be effective. PD-MEMLIN is an empirical feature vector normalization which models clean and noisy spaces by Gaussian Mixture Models (GMMs), and estimates the different compensation linear transformation to be per-formed to clean the signal. In this work the integration of both approaches is proposed. The final design is called PD-MEEMLIN (Phoneme-Dependent Multi-Enviroment Enhanced Models based Linear Normalization), which confirms and improves the effectiv-ness of both approaches. The results obtained show that in very high degraded speech (between -5dB and OdB) PD-MEEMLIN outperforms the SS by a range between 11.4% and 34.5%,for PD-MEMLIN by a range between 11.7% and 24.84%, and for SPLICE by a range between 6.04% and 22.23%. Furthemore, in moderate SNR, i.e. 15 or 20 dB, PD-MEEMLIN is as good as PD-MEMLIN and SS techniques.

Recent Advances in Robust Speech Recognition Technology

Recent Advances in Robust Speech Recognition Technology
Author: Javier Ramírez,Juan Manuel Górriz
Publsiher: Bentham Science
Total Pages: 210
Release: 2011-01-01
ISBN 10: 1608051722
ISBN 13: 9781608051724
Language: EN, FR, DE, ES & NL

Recent Advances in Robust Speech Recognition Technology Book Review:

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 when the acoustical, articulate, or phonetic characteristics of speech in the training and testing environments differ. Obstacles to robust recognition include acoustical degradations produced by additive noise, the effects of linear filtering, nonlinearities in transduction or transmission, as well as impulsive interfering sources, and diminished accuracy caused by changes in articulation produced by the presence of high-intensity noise sources. Although progress over the past decade has been impressive, there are significant obstacles to overcome before speech recognition systems can reach their full potential. Automatic speech recognition (ASR) systems must be robust to all levels, so that they can handle background or channel noise, the occurrence on unfamiliar words, new accents, new users, or unanticipated inputs. They must exhibit more 'intelligence' and integrate speech with other modalities, deriving the user's intent by combining speech with facial expressions, eye movements, gestures, and other input features, and communicating back to the user through multimedia responses. Therefore, as speech recognition technology is transferred from the laboratory to the marketplace, robustness in recognition becomes increasingly significant. This E-book should be useful to computer engineers interested in recent developments in speech recognition technology.

Robust Speech Recognition of Uncertain or Missing Data

Robust Speech Recognition of Uncertain or Missing Data
Author: Dorothea Kolossa,Reinhold Haeb-Umbach
Publsiher: Springer Science & Business Media
Total Pages: 380
Release: 2011-07-14
ISBN 10: 9783642213175
ISBN 13: 3642213170
Language: EN, FR, DE, ES & NL

Robust Speech Recognition of Uncertain or Missing Data Book Review:

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.

Speech and Audio Signal Processing

Speech and Audio Signal Processing
Author: Ben Gold,Nelson Morgan,Dan Ellis
Publsiher: John Wiley & Sons
Total Pages: 661
Release: 2011-08-23
ISBN 10: 0470195363
ISBN 13: 9780470195369
Language: EN, FR, DE, ES & NL

Speech and Audio Signal Processing Book Review:

When Speech and Audio Signal Processing published in 1999,it stood out from its competition in its breadth of coverage andits accessible, intutiont-based style. This book was aimed atindividual students and engineers excited about the broad span ofaudio processing and curious to understand the availabletechniques. Since then, with the advent of the iPod in 2001,the field of digital audio and music has exploded, leading to amuch greater interest in the technical aspects of audioprocessing. This Second Edition will update and revise the originalbook to augment it with new material describing both the enablingtechnologies of digital music distribution (most significantly theMP3) and a range of exciting new research areas in automatic musiccontent processing (such as automatic transcription, musicsimilarity, etc.) that have emerged in the past five years, drivenby the digital music revolution. New chapter topics include: Psychoacoustic Audio Coding, describing MP3 and relatedaudio coding schemes based on psychoacoustic masking ofquantization noise Music Transcription, including automatically derivingnotes, beats, and chords from music signals. Music Information Retrieval, primarily focusing onaudio-based genre classification, artist/style identification, andsimilarity estimation. Audio Source Separation, including multi-microphonebeamforming, blind source separation, and the perception-inspiredtechniques usually referred to as Computational Auditory SceneAnalysis (CASA).

New Era for Robust Speech Recognition

New Era for Robust Speech Recognition
Author: Shinji Watanabe,Marc Delcroix,Florian Metze,John R. Hershey
Publsiher: Springer
Total Pages: 436
Release: 2018-05-24
ISBN 10: 9783319878492
ISBN 13: 3319878492
Language: EN, FR, DE, ES & NL

New Era for Robust Speech Recognition Book Review:

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.

Robust Automatic Speech Recognition and Moduling of Auditory Discrimination with Auditory Experiments Spectro temporal Features

Robust Automatic Speech Recognition and Moduling of Auditory Discrimination with Auditory Experiments Spectro temporal Features
Author: Marc René Schädler
Publsiher: Unknown
Total Pages: 329
Release: 2016
ISBN 10: 9783814223339
ISBN 13: 3814223330
Language: EN, FR, DE, ES & NL

Robust Automatic Speech Recognition and Moduling of Auditory Discrimination with Auditory Experiments Spectro temporal Features Book Review:

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.

Automatic Speech and Speaker Recognition

Automatic Speech and Speaker Recognition
Author: Chin-Hui Lee,Frank K. Soong,Kuldip K. Paliwal
Publsiher: Springer Science & Business Media
Total Pages: 518
Release: 2012-12-06
ISBN 10: 1461313678
ISBN 13: 9781461313670
Language: EN, FR, DE, ES & NL

Automatic Speech and Speaker Recognition Book Review:

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.

Automatic Speech Recognition

Automatic Speech Recognition
Author: Dong Yu,Li Deng
Publsiher: Springer
Total Pages: 321
Release: 2014-11-11
ISBN 10: 1447157796
ISBN 13: 9781447157793
Language: EN, FR, DE, ES & NL

Automatic Speech Recognition Book Review:

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.

Latent Variable Analysis and Signal Separation

Latent Variable Analysis and Signal Separation
Author: Fabian Theis,Andrzej Cichocki,Arie Yeredor,Michael Zibulevsky
Publsiher: Springer Science & Business Media
Total Pages: 538
Release: 2012-03-01
ISBN 10: 3642285503
ISBN 13: 9783642285509
Language: EN, FR, DE, ES & NL

Latent Variable Analysis and Signal Separation Book Review:

This book constitutes the proceedings of the 10th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2012, held in Tel Aviv, Israel, in March 2012. The 20 revised full papers presented together with 42 revised poster papers, 1 keynote lecture, and 2 overview papers for the regular, as well as for the special session were carefully reviewed and selected from numerous submissions. Topics addressed are ranging from theoretical issues such as causality analysis and measures, through novel methods for employing the well-established concepts of sparsity and non-negativity for matrix and tensor factorization, down to a variety of related applications ranging from audio and biomedical signals to precipitation analysis.

Automatic Speech Recognition

Automatic Speech Recognition
Author: Kai-Fu Lee
Publsiher: Springer Science & Business Media
Total Pages: 207
Release: 2012-12-06
ISBN 10: 1461536502
ISBN 13: 9781461536505
Language: EN, FR, DE, ES & NL

Automatic Speech Recognition Book Review:

Speech Recognition has a long history of being one of the difficult problems in Artificial Intelligence and Computer Science. As one goes from problem solving tasks such as puzzles and chess to perceptual tasks such as speech and vision, the problem characteristics change dramatically: knowledge poor to knowledge rich; low data rates to high data rates; slow response time (minutes to hours) to instantaneous response time. These characteristics taken together increase the computational complexity of the problem by several orders of magnitude. Further, speech provides a challenging task domain which embodies many of the requirements of intelligent behavior: operate in real time; exploit vast amounts of knowledge, tolerate errorful, unexpected unknown input; use symbols and abstractions; communicate in natural language and learn from the environment. Voice input to computers offers a number of advantages. It provides a natural, fast, hands free, eyes free, location free input medium. However, there are many as yet unsolved problems that prevent routine use of speech as an input device by non-experts. These include cost, real time response, speaker independence, robustness to variations such as noise, microphone, speech rate and loudness, and the ability to handle non-grammatical speech. Satisfactory solutions to each of these problems can be expected within the next decade. Recognition of unrestricted spontaneous continuous speech appears unsolvable at present. However, by the addition of simple constraints, such as clarification dialog to resolve ambiguity, we believe it will be possible to develop systems capable of accepting very large vocabulary continuous speechdictation.

Advances in Computer Science and Engineering

Advances in Computer Science and Engineering
Author: Hamid Sarbazi-Azad,Behrooz Parhami,Seyed-Ghasem Miremadi,Shaahin Hessabi
Publsiher: Springer Science & Business Media
Total Pages: 1017
Release: 2008-11-23
ISBN 10: 3540899855
ISBN 13: 9783540899853
Language: EN, FR, DE, ES & NL

Advances in Computer Science and Engineering Book Review:

It is our pleasure to welcome you to the proceedings of the 13th International C- puter Society of Iran Computer Conference (CSICC-2008). The conference has been held annually since 1995, except for 1998, when it transitioned from a year-end to first-quarter schedule. It has been moving in the direction of greater selectivity (see Fig.1) and broader international participation. Holding it in Kish Island this year represents an effort to further facilitate and encourage international contributions. We feel privileged to participate in further advancing this strong technical tradition. 60 50 40 30 20 10 0 Dec 23-26 Dec 23-25 Dec 23-25 Jan 26-28 Mar 8-10 Feb 21-23 Feb 28-30 Feb 23-26 Feb 16-19 Feb 15-18 Jan 24-26 Feb 20-22 Mar 9-11 1995 1996 1997 Iran 1999 2000 2001 U of 2002 Iran 2003 2004 2005 Iran 2006 IPM, 2007 2008 Sharif U Amirkabir U of Sharif U Shahid Isfahan, Telecom Ferdowsi Sharif U Telecom Tehran Shahid Sharif U of Tech, U of Tech, Sci/Tech, of Tech, Beheshti Isfahan Res. U, of Tech, Res. Beheshti of Tech, Tehran Tehran Tehran Tehran U, Tehran Center Mashhad Tehran Center U, Tehran Kish Island Dates, Year, Venue

Statistical Language and Speech Processing

Statistical Language and Speech Processing
Author: Thierry Dutoit,Carlos Martín-Vide,Gueorgui Pironkov
Publsiher: Springer
Total Pages: 191
Release: 2018-10-08
ISBN 10: 303000810X
ISBN 13: 9783030008109
Language: EN, FR, DE, ES & NL

Statistical Language and Speech Processing Book Review:

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.

Pattern Recognition in Speech and Language Processing

Pattern Recognition in Speech and Language Processing
Author: Wu Chou,Biing-Hwang Juang
Publsiher: CRC Press
Total Pages: 416
Release: 2003-02-26
ISBN 10: 9780203010525
ISBN 13: 0203010523
Language: EN, FR, DE, ES & NL

Pattern Recognition in Speech and Language Processing Book Review:

Over the last 20 years, approaches to designing speech and language processing algorithms have moved from methods based on linguistics and speech science to data-driven pattern recognition techniques. These techniques have been the focus of intense, fast-moving research and have contributed to significant advances in this field. Pattern Recognition in Speech and Language Processing offers a systematic, up-to-date presentation of these recent developments. It begins with the fundamentals and recent theoretical advances in pattern recognition, with emphasis on classifier design criteria and optimization procedures. The focus then shifts to the application of these techniques to speech processing, with chapters exploring advances in applying pattern recognition to real speech and audio processing systems. The final section of the book examines topics related to pattern recognition in language processing: topics that represent promising new trends with direct impact on information processing systems for the Web, broadcast news, and other content-rich information resources. Each self-contained chapter includes figures, tables, diagrams, and references. The collective effort of experts at the forefront of the field, Pattern Recognition in Speech and Language Processing offers in-depth, insightful discussions on new developments and contains a wealth of information integral to the further development of human-machine communications.

Robust Adaptation to Non Native Accents in Automatic Speech Recognition

Robust Adaptation to Non Native Accents in Automatic Speech Recognition
Author: Silke Goronzy
Publsiher: Springer
Total Pages: 146
Release: 2003-07-01
ISBN 10: 3540362908
ISBN 13: 9783540362906
Language: EN, FR, DE, ES & NL

Robust Adaptation to Non Native Accents in Automatic Speech Recognition Book Review:

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 well as on acoustic features. Furthermore, a specific pronunciation modelling technique that allows the automatic derivation of non-native pronunciations without using non-native data is described and combined with the previous techniques to produce a robust adaptation to non-native accents in an automatic speech recognition system.

Automatic Speech Recognition on Mobile Devices and over Communication Networks

Automatic Speech Recognition on Mobile Devices and over Communication Networks
Author: Zheng-Hua Tan,Boerge Lindberg
Publsiher: Springer Science & Business Media
Total Pages: 402
Release: 2008-04-17
ISBN 10: 1848001436
ISBN 13: 9781848001435
Language: EN, FR, DE, ES & NL

Automatic Speech Recognition on Mobile Devices and over Communication Networks Book Review:

The advances in computing and networking have sparked an enormous interest in deploying automatic speech recognition on mobile devices and over communication networks. This book brings together academic researchers and industrial practitioners to address the issues in this emerging realm and presents the reader with a comprehensive introduction to the subject of speech recognition in devices and networks. It covers network, distributed and embedded speech recognition systems.

Robustness in Language and Speech Technology

Robustness in Language and Speech Technology
Author: Jean-Claude Junqua,Gertjan van Noord
Publsiher: Springer Science & Business Media
Total Pages: 269
Release: 2001-02-28
ISBN 10: 9780792367901
ISBN 13: 0792367901
Language: EN, FR, DE, ES & NL

Robustness in Language and Speech Technology Book Review:

In this book we address robustness issues at the speech recognition and natural language parsing levels, with a focus on feature extraction and noise robust recognition, adaptive systems, language modeling, parsing, and natural language understanding. This book attempts to give a clear overview of the main technologies used in language and speech processing, along with an extensive bibliography to enable topics of interest to be pursued further. It also brings together speech and language technologies often considered separately. Robustness in Language and Speech Technology 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.