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Author : Yves Normandin
Publisher : National Library of Canada = Bibliothèque nationale du Canada
Page : 180 pages
File Size : 37,91 MB
Release : 1991
Category : Automatic speech recognition
ISBN :
Author : Przemyslaw Dymarski
Publisher : BoD – Books on Demand
Page : 329 pages
File Size : 35,67 MB
Release : 2011-04-19
Category : Computers
ISBN : 9533072083
Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research.
Author : Xiadong He
Publisher : Morgan & Claypool Publishers
Page : 121 pages
File Size : 50,95 MB
Release : 2008
Category : Automatic speech recognition
ISBN : 1598293087
In this book, we introduce the background and mainstream methods of probabilistic modeling and discriminative parameter optimization for speech recognition. The specific models treated in depth include the widely used exponential-family distributions and the hidden Markov model. A detailed study is presented on unifying the common objective functions for discriminative learning in speech recognition, namely maximum mutual information (MMI), minimum classification error, and minimum phone/word error. The unification is presented, with rigorous mathematical analysis, in a common rational-function form. This common form enables the use of the growth transformation (or extended Baum-Welch) optimization framework in discriminative learning of model parameters. In addition to all the necessary introduction of the background and tutorial material on the subject, we also included technical details on the derivation of the parameter optimization formulas for exponential-family distributions, discrete hidden Markov models (HMMs), and continuous-density HMMs in discriminative learning. Selected experimental results obtained by the authors in firsthand are presented to show that discriminative learning can lead to superior speech recognition performance over conventional parameter learning. Details on major algorithmic implementation issues with practical significance are provided to enable the practitioners to directly reproduce the theory in the earlier part of the book into engineering practice.
Author : Mark Gales
Publisher : Now Publishers Inc
Page : 125 pages
File Size : 44,87 MB
Release : 2008
Category : Automatic speech recognition
ISBN : 1601981201
The Application of Hidden Markov Models in Speech Recognition presents the core architecture of a HMM-based LVCSR system and proceeds to describe the various refinements which are needed to achieve state-of-the-art performance.
Author : Frederick Jelinek
Publisher : MIT Press
Page : 324 pages
File Size : 36,84 MB
Release : 1998-01-15
Category : Language Arts & Disciplines
ISBN : 9780262100663
This book reflects decades of important research on the mathematical foundations of speech recognition. It focuses on underlying statistical techniques such as hidden Markov models, decision trees, the expectation-maximization algorithm, information theoretic goodness criteria, maximum entropy probability estimation, parameter and data clustering, and smoothing of probability distributions. The author's goal is to present these principles clearly in the simplest setting, to show the advantages of self-organization from real data, and to enable the reader to apply the techniques.
Author : Yochai Konig
Publisher :
Page : 218 pages
File Size : 27,18 MB
Release : 1996
Category :
ISBN :
Author : Chi Hau Chen
Publisher : World Scientific
Page : 652 pages
File Size : 11,17 MB
Release : 2005-01-14
Category : Computers
ISBN : 9814481319
The book provides an up-to-date and authoritative treatment of pattern recognition and computer vision, with chapters written by leaders in the field. On the basic methods in pattern recognition and computer vision, topics range from statistical pattern recognition to array grammars to projective geometry to skeletonization, and shape and texture measures. Recognition applications include character recognition and document analysis, detection of digital mammograms, remote sensing image fusion, and analysis of functional magnetic resonance imaging data, etc. There are six chapters on current activities in human identification. Other topics include moving object tracking, performance evaluation, content-based video analysis, musical style recognition, number plate recognition, etc.
Author : Frederick Jelinek
Publisher : MIT Press
Page : 307 pages
File Size : 42,4 MB
Release : 2022-11-01
Category : Language Arts & Disciplines
ISBN : 0262546604
This book reflects decades of important research on the mathematical foundations of speech recognition. It focuses on underlying statistical techniques such as hidden Markov models, decision trees, the expectation-maximization algorithm, information theoretic goodness criteria, maximum entropy probability estimation, parameter and data clustering, and smoothing of probability distributions. The author's goal is to present these principles clearly in the simplest setting, to show the advantages of self-organization from real data, and to enable the reader to apply the techniques. Bradford Books imprint
Author : Keith Ponting
Publisher : Springer Science & Business Media
Page : 478 pages
File Size : 47,4 MB
Release : 2012-12-06
Category : Computers
ISBN : 3642600875
Proceedings of the NATO Advanced Study Institute on Computational Models of Speech Pattern Processing, held in St. Helier, Jersey, UK, July 7-18, 1997
Author : Nizar Bouguila
Publisher : Springer Nature
Page : 303 pages
File Size : 31,74 MB
Release : 2022-05-19
Category : Technology & Engineering
ISBN : 3030991423
This book focuses on recent advances, approaches, theories, and applications related Hidden Markov Models (HMMs). In particular, the book presents recent inference frameworks and applications that consider HMMs. The authors discuss challenging problems that exist when considering HMMs for a specific task or application, such as estimation or selection, etc. The goal of this volume is to summarize the recent advances and modern approaches related to these problems. The book also reports advances on classic but difficult problems in HMMs such as inference and feature selection and describes real-world applications of HMMs from several domains. The book pertains to researchers and graduate students, who will gain a clear view of recent developments related to HMMs and their applications.