000			02717nam a2200373 i 4500
001			CR9781107295360
003			UkCbUP
005			20240730160759.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			130705s2015||||enk o ||1 0|eng|d
020			_a9781107295360 (ebook)
020			_z9781107055575 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aP53.815 _b.W38 2015
082	0	0	_a410.1/51 _223
100	1		_aWatanabe, Shinji _c(Communications engineer), _eauthor. _974679
245	1	0	_aBayesian speech and language processing / _cShinji Watanabe, Jen-Tzung Chien.
246	3		_aBayesian speech & language processing
264		1	_aCambridge : _bCambridge University Press, _c2015.
300			_a1 online resource (xxi, 424 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
520			_aWith 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.
505	8		_aMachine generated contents note: Part 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.
650		0	_aLanguage and languages _xStudy and teaching _xStatistical method. _974680
650		0	_aBayesian statistical decision theory. _98233
700	1		_aChien, Jen-Tzung, _eauthor. _974681
776	0	8	_iPrint version: _z9781107055575
856	4	0	_uhttps://doi.org/10.1017/CBO9781107295360
942			_cEBK
999			_c84207 _d84207