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Bayesian speech and language processing / Shinji Watanabe, Jen-Tzung Chien.

By: Watanabe, Shinji (Communications engineer) [author.].
Contributor(s): Chien, Jen-Tzung [author.].
Material type: materialTypeLabelBookPublisher: Cambridge : Cambridge University Press, 2015Description: 1 online resource (xxi, 424 pages) : digital, PDF file(s).Content type: text Media type: computer Carrier type: online resourceISBN: 9781107295360 (ebook).Other title: Bayesian speech & language processing.Subject(s): Language and languages -- Study and teaching -- Statistical method | Bayesian statistical decision theoryAdditional physical formats: Print version: : No titleDDC classification: 410.1/51 Online resources: Click here to access online
Contents:
Machine 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.
Summary: 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.
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Title from publisher's bibliographic system (viewed on 05 Oct 2015).

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.

Machine 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.

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