03063cam a22003498i 4500 94 94 18465322 OSt 20190819143748.0 150127s2015 enk 000 0 eng 2014050265 9781107055575 (hardback) DLC eng DLC rda pcc P53.815 .W38 2015 006.454 WAT C;1 23 Watanabe, Shinji (Communications engineer) author. Bayesian speech and language processing / Shinji Watanabe, Mitsubishi Electric Research Laboratories ; Jen-Tzung Chien ; National Chiao Tung University. 1111 Cambridge : Cambridge University Press, 2015. pages cm text rdacontent unmediated rdamedia volume rdacarrier 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. "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"-- Provided by publisher. "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"-- Provided by publisher. Language and languages Study and teaching Statistical methods. Bayesian statistical decision theory. 0 vip orignew 1 ecip 20 y-gencatlg ddc STS 0 0 ddc 0 0 4/2 AIISH AIISH 2019-08-19 0 006.454 WAT C;1 20841 2019-08-19 00:00:00 2019-08-19 STS 0 0 ddc 0 0 R-1/5 AIISH AIISH 2020-01-25 0 006.454 WAT C 20840 2020-01-25 00:00:00 2020-01-25 RFT