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This book comprehensively presents an unconventional quantum criticality caused by valence fluctuations, which offers theoretical understanding of unconventional Fermi-liquid properties in cerium- and ytterbium-based heavy fermion metals including CeCu2(Si,Ge)2 and CeRhIn5 under pressure, and quasicrystal β-YbAlB4 and Yb15Al34Au51. The book begins with an introduction to fundamental concepts for heavy fermion systems, valence fluctuation, and quantum phase transition, including self-consistent renormalization group theory. A subsequent chapter is devoted to a comprehensive description of the theory of the unconventional quantum criticality based on a valence transition, featuring explicit temperature dependence of various physical quantities, which allows for comparisons to relevant experiments. Lastly, it discusses how ubiquitous the valence fluctuation is, presenting candidate materials not only in heavy fermions, but also in strongly correlated electrons represented by high-Tc superconductor cuprates. Introductory chapters provide useful materials for learning fundamentals of heavy fermion systems and their theory. Further, experimental topics relevant to valence fluctuations are valuable resources for those who are new to the field to easily catch up with experimental background and facts.
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With this comprehensive guide you will learn how to apply Bayesian machine learning techniques systematically to solve various problems in speech and language processing. A range of statistical models is detailed, from hidden Markov models to Gaussian mixture models, n-gram models and latent topic models, along with applications including automatic speech recognition, speaker verification, and information retrieval. Approximate Bayesian inferences based on MAP, Evidence, Asymptotic, VB, and MCMC approximations are provided as well as full derivations of calculations, useful notations, formulas, and rules. The authors address the difficulties of straightforward applications and provide detailed examples and case studies to demonstrate how you can successfully use practical Bayesian inference methods to improve the performance of information systems. This is an invaluable resource for students, researchers, and industry practitioners working in machine learning, signal processing, and speech and language processing.
Language and languages --- Bayesian statistical decision theory. --- Study and teaching --- Statistical method.
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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.
Computer science. --- Artificial intelligence. --- Computational linguistics. --- Linguistics. --- Computer Science. --- Artificial Intelligence (incl. Robotics). --- Signal, Image and Speech Processing. --- Language Translation and Linguistics. --- Linguistics, general. --- Linguistic science --- Science of language --- Language and languages --- Automatic language processing --- Language data processing --- Linguistics --- Natural language processing (Linguistics) --- Applied linguistics --- Cross-language information retrieval --- Mathematical linguistics --- Multilingual computing --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Informatics --- Science --- Data processing --- Natural language processing (Computer science). --- Artificial Intelligence. --- Natural Language Processing (NLP). --- NLP (Computer science) --- Artificial intelligence --- Human-computer interaction --- Semantic computing --- Signal processing. --- Image processing. --- Speech processing systems. --- Computational linguistics --- Electronic systems --- Information theory --- Modulation theory --- Oral communication --- Speech --- Telecommunication --- Singing voice synthesizers --- Pictorial data processing --- Picture processing --- Processing, Image --- Imaging systems --- Optical data processing --- Processing, Signal --- Information measurement --- Signal theory (Telecommunication)
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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.
Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- Linguistics --- akoestiek --- beeldverwerking --- NLP (neurolinguïstisch programmeren) --- spraaktechnologie --- deep learning --- linguïstiek --- KI (kunstmatige intelligentie) --- signaalverwerking
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