000			04275nam a22004335i 4500
999			_c387591 _d387591
001			387591
003			ES-MaUEC
005			20230327153131.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			220601s2008 sz | s |||| 0|eng d
020			_a9783031025570
024	7		_a10.1007/978-3-031-02557-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK7895.S65 _b2008 EB
100	1		_aHe, Xiadong _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687741
245	1	0	_aDiscriminative Learning for Speech Recognition : _bTheory and Practice _cby Xiadong He, Li Deng
250			_a1st edition 2008
264		1	_aCham _bSpringer International Publishing _c2008
300			_a1 recurso en línea (VII, 112 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Speech and Audio Processing _x1932-1678
505	0		_aIntroduction and Background -- Statistical Speech Recognition: A Tutorial -- Discriminative Learning: A Unified Objective Function -- Discriminative Learning Algorithm for Exponential-Family Distributions -- Discriminative Learning Algorithm for Hidden Markov Model -- Practical Implementation of Discriminative Learning -- Selected Experimental Results -- Epilogue -- Major Symbols Used in the Book and Their Descriptions -- Mathematical Notation -- Bibliography.
520			_aIn 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. Table of Contents: Introduction and Background / Statistical Speech Recognition: A Tutorial / Discriminative Learning: A Unified Objective Function / Discriminative Learning Algorithm for Exponential-Family Distributions / Discriminative Learning Algorithm for Hidden Markov Model / Practical Implementation of Discriminative Learning / Selected Experimental Results / Epilogue / Major Symbols Used in the Book and Their Descriptions / Mathematical Notation / Bibliography.
988			_aSynthesis Collection of Technology_2008
650		7	_2embne _9147323 _aReconocimiento automático del lenguaje _xMétodos estadísticos
650		7	_2embne _9675808 _aVoz _xMedición _xProceso de datos
650		7	_2embne _9140923 _aTecnología educativa
700	1		_aDeng, Li, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687655 _d1958-
776	0	8	_iPrinted edition: _z9783031014291
776	0	8	_iPrinted edition: _z9783031036859
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02557-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI