Bayesian Speech And Language Processing

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Bayesian Speech And Language Processing

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Author : Shinji Watanabe
language : en
Publisher: Cambridge University Press
Release Date : 2015-07-15

Bayesian Speech And Language Processing written by Shinji Watanabe and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-07-15 with Computers categories.

A practical and comprehensive guide on how to apply Bayesian machine learning techniques to solve speech and language processing problems.

Speech And Language Processing

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Author : Daniel Jurafsky
language : en
Publisher:
Release Date : 2000-01

Speech And Language Processing written by Daniel Jurafsky and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-01 with Automatic speech recognition categories.

This book takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora.Methodology boxes are included in each chapter. Each chapter is built around one or more worked examples to demonstrate the main idea of the chapter. Covers the fundamental algorithms of various fields, whether originally proposed for spoken or written language to demonstrate how the same algorithm can be used for speech recognition and word-sense disambiguation. Emphasis on web and other practical applications. Emphasis on scientific evaluation. Useful as a reference for professionals in any of the areas of speech and language processing.

Numerical Bayesian Methods Applied To Signal Processing

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Author : Joseph J.K. O Ruanaidh
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06

Numerical Bayesian Methods Applied To Signal Processing written by Joseph J.K. O Ruanaidh and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.

This book is concerned with the processing of signals that have been sam pled and digitized. The fundamental theory behind Digital Signal Process ing has been in existence for decades and has extensive applications to the fields of speech and data communications, biomedical engineering, acous tics, sonar, radar, seismology, oil exploration, instrumentation and audio signal processing to name but a few [87]. The term "Digital Signal Processing", in its broadest sense, could apply to any operation carried out on a finite set of measurements for whatever purpose. A book on signal processing would usually contain detailed de scriptions of the standard mathematical machinery often used to describe signals. It would also motivate an approach to real world problems based on concepts and results developed in linear systems theory, that make use of some rather interesting properties of the time and frequency domain representations of signals. While this book assumes some familiarity with traditional methods the emphasis is altogether quite different. The aim is to describe general methods for carrying out optimal signal processing.

Source Separation And Machine Learning

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Author : Jen-Tzung Chien
language : en
Publisher: Academic Press
Release Date : 2018-10-16

Source Separation And Machine Learning written by Jen-Tzung Chien and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-16 with Technology & Engineering categories.

Source Separation and Machine Learning presents the fundamentals in adaptive learning algorithms for Blind Source Separation (BSS) and emphasizes the importance of machine learning perspectives. It illustrates how BSS problems are tackled through adaptive learning algorithms and model-based approaches using the latest information on mixture signals to build a BSS model that is seen as a statistical model for a whole system. Looking at different models, including independent component analysis (ICA), nonnegative matrix factorization (NMF), nonnegative tensor factorization (NTF), and deep neural network (DNN), the book addresses how they have evolved to deal with multichannel and single-channel source separation. - Emphasizes the modern model-based Blind Source Separation (BSS) which closely connects the latest research topics of BSS and Machine Learning - Includes coverage of Bayesian learning, sparse learning, online learning, discriminative learning and deep learning - Presents a number of case studies of model-based BSS (categorizing them into four modern models - ICA, NMF, NTF and DNN), using a variety of learning algorithms that provide solutions for the construction of BSS systems

Multilingual Natural Language Processing Applications

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Author : Daniel Bikel
language : en
Publisher: IBM Press
Release Date : 2012-05-11

Multilingual Natural Language Processing Applications written by Daniel Bikel and has been published by IBM Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-05-11 with Business & Economics categories.

Multilingual Natural Language Processing Applications is the first comprehensive single-source guide to building robust and accurate multilingual NLP systems. Edited by two leading experts, it integrates cutting-edge advances with practical solutions drawn from extensive field experience. Part I introduces the core concepts and theoretical foundations of modern multilingual natural language processing, presenting today’s best practices for understanding word and document structure, analyzing syntax, modeling language, recognizing entailment, and detecting redundancy. Part II thoroughly addresses the practical considerations associated with building real-world applications, including information extraction, machine translation, information retrieval/search, summarization, question answering, distillation, processing pipelines, and more. This book contains important new contributions from leading researchers at IBM, Google, Microsoft, Thomson Reuters, BBN, CMU, University of Edinburgh, University of Washington, University of North Texas, and others. Coverage includes Core NLP problems, and today’s best algorithms for attacking them Processing the diverse morphologies present in the world’s languages Uncovering syntactical structure, parsing semantics, using semantic role labeling, and scoring grammaticality Recognizing inferences, subjectivity, and opinion polarity Managing key algorithmic and design tradeoffs in real-world applications Extracting information via mention detection, coreference resolution, and events Building large-scale systems for machine translation, information retrieval, and summarization Answering complex questions through distillation and other advanced techniques Creating dialog systems that leverage advances in speech recognition, synthesis, and dialog management Constructing common infrastructure for multiple multilingual text processing applications This book will be invaluable for all engineers, software developers, researchers, and graduate students who want to process large quantities of text in multiple languages, in any environment: government, corporate, or academic.

Modern Methodology And Applications In Spatial Temporal Modeling

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Author : Gareth William Peters
language : en
Publisher: Springer
Release Date : 2016-01-08

Modern Methodology And Applications In Spatial Temporal Modeling written by Gareth William Peters and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-01-08 with Mathematics categories.

​ This book provides a modern introductory tutorial on specialized methodological and applied aspects of spatial and temporal modeling. The areas covered involve a range of topics which reflect the diversity of this domain of research across a number of quantitative disciplines. For instance, the first chapter deals with non-parametric Bayesian inference via a recently developed framework known as kernel mean embedding which has had a significant influence in machine learning disciplines. The second chapter takes up non-parametric statistical methods for spatial field reconstruction and exceedance probability estimation based on Gaussian process-based models in the context of wireless sensor network data. The third chapter presents signal-processing methods applied to acoustic mood analysis based on music signal analysis. The fourth chapter covers models that are applicable to time series modeling in the domain of speech and language processing. This includes aspects of factor analysis, independent component analysis in an unsupervised learning setting. The chapter moves on to include more advanced topics on generalized latent variable topic models based on hierarchical Dirichlet processes which recently have been developed in non-parametric Bayesian literature. The final chapter discusses aspects of dependence modeling, primarily focusing on the role of extreme tail-dependence modeling, copulas, and their role in wireless communications system models.

Machine Learning For Speaker Recognition

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Author : Man-Wai Mak
language : en
Publisher: Cambridge University Press
Release Date : 2020-11-19

Machine Learning For Speaker Recognition written by Man-Wai Mak and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-19 with Computers categories.

Learn fundamental and advanced machine learning techniques for robust speaker recognition and domain adaptation with this useful toolkit.

Source Separation In Physical Chemical Sensing

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Author : Christian Jutten
language : en
Publisher: John Wiley & Sons
Release Date : 2023-10-23

Source Separation In Physical Chemical Sensing written by Christian Jutten and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-23 with Technology & Engineering categories.

Source Separation in Physical-Chemical Sensing Master advanced signal processing for enhanced physical and chemical sensors with this essential guide In many domains (medicine, satellite imaging and remote sensing, food industry, materials science), data is obtained from large sets of physical/chemical sensors or sensor arrays. Such sophisticated measurement techniques require advanced and smart processing for extracting useful information from raw sensing data. Usually, sensors are not very selective and record a mixture of the useful latent variables. An innovative technique called Blind Source Separation (BSS) can isolate and retrieve the individual latent variables from a mixed-source data array, allowing for refined analysis that fully exploits these cutting-edged imaging and signal-sensing technologies. Source Separation in Physical-Chemical Sensing, supplies a thorough introduction to the principles of BSS, main methods and algorithms and its potential applications in various domains where data are obtained through physical or chemical sensors. Designed to bridge the gap between chemical/physical analysis and signal processing, it promises to be invaluable in many fields. Its alertness to the latest technologies and the full range of potential BSS applications make it an indispensable introduction to this cutting-edge method. Source Separation in Physical-Chemical Sensing readers will also find: BSS examples on chemical and physical sensors and devices to enhance processing and analysis. Detailed treatment of source separation in potentiometric sensors, ion-sensitive sensors, hyperspectral imaging, Raman and fluorescence spectroscopy, chromatography, and others. Thorough discussion of Bayesian source separation, nonnegative matrix factorization, tensorial methods, geometrical methods, constrained optimization, and more. Source Separation in Physical-Chemical Sensing is a must-have for researchers and engineers working in signal processing and statistical analysis, as well as for chemists, physicists or engineers looking to apply source separation in various application domains.

Computational Collective Intelligence Technologies And Applications

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Author : Jeng-Shyang Pan
language : en
Publisher: Springer
Release Date : 2010-11-06

Computational Collective Intelligence Technologies And Applications written by Jeng-Shyang Pan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-11-06 with Computers categories.

This volume composes the proceedings of the Second International Conference on Computational Collective Intelligence––Technologies and Applications (ICCCI 2010), which was hosted by National Kaohsiung University of Applied Sciences and Wroclaw University of Technology, and was held in Kaohsiung City on November 10-12, 2010. ICCCI 2010 was technically co-sponsored by Shenzhen Graduate School of Harbin Institute of Technology, the Tainan Chapter of the IEEE Signal Processing Society, the Taiwan Association for Web Intelligence Consortium and the Taiwanese Association for Consumer Electronics. It aimed to bring together researchers, engineers and po- cymakers to discuss the related techniques, to exchange research ideas, and to make friends. ICCCI 2010 focused on the following themes: • Agent Theory and Application • Cognitive Modeling of Agent Systems • Computational Collective Intelligence • Computer Vision • Computational Intelligence • Hybrid Systems • Intelligent Image Processing • Information Hiding • Machine Learning • Social Networks • Web Intelligence and Interaction Around 500 papers were submitted to ICCCI 2010 and each paper was reviewed by at least two referees. The referees were from universities and industrial organizations. 155 papers were accepted for the final technical program. Four plenary talks were kindly offered by: Gary G. Yen (Oklahoma State University, USA), on “Population Control in Evolutionary Multi-objective Optimization Algorithm,” Chin-Chen Chang (Feng Chia University, Taiwan), on “Applying De-clustering Concept to Information Hiding,” Qinyu Zhang (Harbin Institute of Technology, China), on “Cognitive Radio Networks and Its Applications,” and Lakhmi C.

The Lexical Typology Of Semantic Shifts

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Author : Päivi Juvonen
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2016-08-08

The Lexical Typology Of Semantic Shifts written by Päivi Juvonen and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-08 with Language Arts & Disciplines categories.

The volume focuses on semantic shifts and motivation patterns in the lexicon. Its key feature is its lexico-typological orientation, i.e. a heavy emphasis on systematic cross-linguistic comparison. The book presents current theoretical and methodological trends in the study of semantic shifts and motivational patters based on an abundance of empirical findings across genetically, areally and typologically diverse languages.