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Robust Automatic Speech Recognition

Robust Automatic Speech Recognition
  • Author : Jinyu Li,Li Deng,Reinhold Haeb-Umbach,Yifan Gong
  • Publisher :Unknown
  • Release Date :2015-10-30
  • Total pages :306
  • ISBN : 9780128026168
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Summary : 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
  • Publisher :Unknown
  • Release Date :2012-09-19
  • Total pages :520
  • ISBN : 9781118392669
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Summary : 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

Acoustical and Environmental Robustness in Automatic Speech Recognition

Acoustical and Environmental Robustness in Automatic Speech Recognition
  • Author : Alex Acero
  • Publisher :Unknown
  • Release Date :1992-11-30
  • Total pages :186
  • ISBN : 0792392841
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Summary : 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.

Robustness in Automatic Speech Recognition

Robustness in Automatic Speech Recognition
  • Author : Jean-Claude Junqua,Jean-Paul Haton
  • Publisher :Unknown
  • Release Date :2012-12-06
  • Total pages :440
  • ISBN : 9781461312970
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Summary : 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.

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
  • Publisher :Unknown
  • Release Date :2006
  • Total pages :229
  • ISBN : OCLC:970545486
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Summary : 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.

New Era for Robust Speech Recognition

New Era for Robust Speech Recognition
  • Author : Shinji Watanabe,Marc Delcroix,Florian Metze,John R. Hershey
  • Publisher :Unknown
  • Release Date :2017-10-30
  • Total pages :436
  • ISBN : 9783319646800
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Summary : 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 Adaptation to Non-Native Accents in Automatic Speech Recognition

Robust Adaptation to Non-Native Accents in Automatic Speech Recognition
  • Author : Silke Goronzy
  • Publisher :Unknown
  • Release Date :2003-07-01
  • Total pages :146
  • ISBN : 9783540362906
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Summary : 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.

Robust Automatic Recognition of Birdsongs and Human Speech: a Template-Based Approach

Robust Automatic Recognition of Birdsongs and Human Speech: a Template-Based Approach
  • Author : Kantapon Kaewtip
  • Publisher :Unknown
  • Release Date :2017
  • Total pages :135
  • ISBN : OCLC:1047735720
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Summary : This dissertation focuses on robust signal processing algorithms for birdsongs and speech signals. Automatic phrase or syllable detection systems of bird sounds are useful in several applications. However, bird-phrase detection is challenging due to segmentation error, duration variability, limited training data, and background noise. Two spectrograms with identical class labels may look different due to time misalignment and frequency variation. In real recording environments such as in a forest, the data can be corrupted by background interference, such as rain, wind, other animals or even other birds vocalizing. A noise-robust classifier needs to handle such conditions. Similarly, Automatic Speech Recognition (ASR) works well in quiet environments, but a large degradation in performance is observed when the speech signal is corrupted by background noise. The ASR performance would benefit from robust representations of speech signals and from robust recognition systems. The first topic of this dissertation focuses on an automatic birdsong-phrase recognition system that is robust to limited training data, class variability, and noise. The algorithm comprises a noise-robust Dynamic-Time-Warping (DTW)- based segmentation and a discriminative classifier for outlier rejection. The algorithm utilizes DTW and prominent (high energy) time-frequency regions of training spectrograms to derive a reliable noise-robust template for each phrase class. The resulting template is then used for segmenting continuous recordings to obtain segment candidates whose spectrogram amplitudes in the prominent regions are used as features to a Support Vector Machine (SVM). In addition, we present a novel approach to training HMMs with extremely limited data. First, the algorithm learns the Global Gaussian Mixture Models (GMMs) for all training phrases available. GMM parameters are then used to initialize state parameters of each individual model. The number of states and the mixture components for each state are determined by the acoustic variation of each phrase type. The (high-energy) time-frequency prominent regions are used to compute the state emitting probability to increase noise-robustness. The second topic of the dissertation deals with noise-robust processing for automatic speech recognition. We also propose a new pitch-based spectral enhancement algorithm based on voiced frames for speech analysis and noise-robust speech processing. The proposed algorithm determines a time-warping function (TWF) and the speaker's pitch with high precision, simultaneously. This technique reduces the smearing effect in between harmonics when the fundamental frequency is not constant within the analysis window. To do so, we propose a metric called the harmonic residual which measures the difference between the actual spectrum and the resynthesized spectrum derived from the linear model of speech production with various combinations of TWF and high-precision pitch values as parameters. The TWF and pitch pair that yields the minimum harmonic residual is selected and the enhanced spectrum is obtained accordingly. We show how this new representation can be also used for automatic speech recognition by proposing a robust spectral representation derived from harmonic amplitude interpolation.

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
  • Publisher :Unknown
  • Release Date :2016
  • Total pages :229
  • ISBN : 3814223330
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Summary : 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
  • Publisher :Unknown
  • Release Date :2012-12-06
  • Total pages :518
  • ISBN : 9781461313670
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Summary : 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
  • Publisher :Unknown
  • Release Date :2014-11-11
  • Total pages :321
  • ISBN : 9781447157793
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Summary : 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
  • Publisher :Unknown
  • Release Date :2012-03-01
  • Total pages :538
  • ISBN : 9783642285509
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Summary : 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
  • Publisher :Unknown
  • Release Date :2012-12-06
  • Total pages :207
  • ISBN : 9781461536505
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Summary : 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.

Pattern Recognition in Speech and Language Processing

Pattern Recognition in Speech and Language Processing
  • Author : Wu Chou,Biing-Hwang Juang
  • Publisher :Unknown
  • Release Date :2003-02-26
  • Total pages :416
  • ISBN : 0203010523
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Summary : 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.

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
  • Publisher :Unknown
  • Release Date :2008-04-17
  • Total pages :402
  • ISBN : 9781848001435
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Summary : 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.

Text, Speech, and Dialogue

Text, Speech, and Dialogue
  • Author : Kamil Ekštein
  • Publisher :Unknown
  • Release Date :2019-10-02
  • Total pages :414
  • ISBN : 9783030279479
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Summary : This book constitutes the proceedings of the 22nd International Conference on Text, Speech, and Dialogue, TSD 2019, held in Ljubljana, Slovenia, in September 2019. The 33 full papers presented in this volume were carefully reviewed and selected from 73 submissions. They were organized in topical sections named text and speech. The book also contains one invited talk in full paper length.

Robust Speaker Recognition in Noisy Environments

Robust Speaker Recognition in Noisy Environments
  • Author : K. Sreenivasa Rao,Sourjya Sarkar
  • Publisher :Unknown
  • Release Date :2014-06-21
  • Total pages :139
  • ISBN : 9783319071305
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Summary : This book discusses speaker recognition methods to deal with realistic variable noisy environments. The text covers authentication systems for; robust noisy background environments, functions in real time and incorporated in mobile devices. The book focuses on different approaches to enhance the accuracy of speaker recognition in presence of varying background environments. The authors examine: (a) Feature compensation using multiple background models, (b) Feature mapping using data-driven stochastic models, (c) Design of super vector- based GMM-SVM framework for robust speaker recognition, (d) Total variability modeling (i-vectors) in a discriminative framework and (e) Boosting method to fuse evidences from multiple SVM models.

Robust Speech Recognition and Understanding

Robust Speech Recognition and Understanding
  • Author : Danel Jaso
  • Publisher :Unknown
  • Release Date :2016-04-01
  • Total pages :276
  • ISBN : 1681174669
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Summary : "Speech recognition systems have become much more robust in recent years with respect to both speaker variability and acoustical variability. 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. In addition to achieving speaker independence, many current systems can also automatically compensate for modest amounts of acoustical degradation caused by the effects of unknown noise and unknown linear filtering. As speech recognition and spoken language technologies are being transferred to real applications, the need for greater robustness in recognition technology is becoming increasingly apparent. Substantial progress has also been made over the last decade in the dynamic adaptation of speech recognition systems to new speakers, with techniques that modify or warp the systems' phonetic representations to reflect the acoustical characteristics of individual speakers. Speech recognition systems have also become more robust in recent years, particularly with regard to slowly-varying acoustical sources of degradation. 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.Robust Speech Recognition and Understanding brings together many different aspects of the current research on automatic speech recognition and language understanding. Additionally, it presents a comprehensive survey of the state-ofthe-art in techniques used to improve the robustness of speech recognition systems to these degrading external influences. "

Distant Speech Recognition

Distant Speech Recognition
  • Author : Matthias Woelfel,John McDonough
  • Publisher :Unknown
  • Release Date :2009-04-20
  • Total pages :600
  • ISBN : 9780470714072
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Summary : 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.

Modern Fuzzy Control Systems and Its Applications

Modern Fuzzy Control Systems and Its Applications
  • Author : S. Ramakrishnan
  • Publisher :Unknown
  • Release Date :2017-08-30
  • Total pages :466
  • ISBN : 9789535133896
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Summary : Control systems play an important role in engineering. Fuzzy logic is the natural choice for designing control applications and is the most popular and appropriate for the control of home and industrial appliances. Academic and industrial experts are constantly researching and proposing innovative and effective fuzzy control systems. This book is an edited volume and has 21 innovative chapters arranged into five sections covering applications of fuzzy control systems in energy and power systems, navigation systems, imaging, and industrial engineering. Overall, this book provides a rich set of modern fuzzy control systems and their applications and will be a useful resource for the graduate students, researchers, and practicing engineers in the field of electrical engineering.

Robustness in Language and Speech Technology

Robustness in Language and Speech Technology
  • Author : Jean-Claude Junqua,Gertjan van Noord
  • Publisher :Unknown
  • Release Date :2001-02-28
  • Total pages :269
  • ISBN : 0792367901
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Summary : 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.