000			03063cam a22003498i 4500
999			_c94 _d94
001			18465322
003			OSt
005			20190819143748.0
008			150127s2015 enk 000 0 eng
010			_a 2014050265
020			_a9781107055575 (hardback)
040			_aDLC _beng _cDLC _erda
042			_apcc
050	0	0	_aP53.815 _b.W38 2015
082	0	0	_a006.454 WAT C;1 _223
100	1		_aWatanabe, Shinji _c(Communications engineer) _eauthor.
245	1	0	_aBayesian speech and language processing / _cShinji Watanabe, Mitsubishi Electric Research Laboratories ; Jen-Tzung Chien ; National Chiao Tung University.
263			_a1111
264		1	_aCambridge : _bCambridge University Press, _c2015.
300			_apages cm
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
338			_avolume _2rdacarrier
505	8		_aPart I. General Discussion: 1. Introduction; 2. Bayesian approach; 3. Statistical models in speech and language processing; Part II. Approximate Inference: 4. Maximum a posteriori approximation; 5. Evidence approximation; 6. Asymptotic approximation; 7. Variational Bayes; 8. Markov chain Monte Carlo.
520			_a"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"-- _cProvided by publisher.
520			_a"In general, speech and language processing involves extensive knowledge of statistical models. The acoustic model using hidden Markov models and language model using n-grams are mainly introduced. Both acoustic and language models are important parts of modern speech recognition systems where the learned models from real-world data are full of complexity, ambiguity and uncertainty. The uncertainty modeling is crucial to tackle the lack of robustness for speech and language processing"-- _cProvided by publisher.
650		0	_aLanguage and languages _xStudy and teaching _xStatistical methods.
650		0	_aBayesian statistical decision theory.
906			_a0 _bvip _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _cSTS