Bayesian Speech and Language Processing
$77.00 ( ) USD
- Shinji Watanabe, Mitsubishi Electric Research Laboratories, Cambridge, Massachusetts
- Jen-Tzung Chien, National Chiao Tung University, Taiwan
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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.Read more
- Provides practical advice for applying Bayesian machine learning techniques to solve speech and language processing problems
- Includes a systematic survey of Bayesian theories with comprehensive examples and case studies
- Details a range of important statistical models and their applications
Reviews & endorsements
"This book provides an overview of a wide range of fundamental theories of Bayesian learning, inference, and prediction for uncertainty modeling in speech and language processing. The uncertainty modeling is crucial in increasing the robustness of practical systems based on statistical modeling under real environments, such as automatic speech recognition systems under noise, and question answering systems based on limited size of training data. This is the most advanced and comprehensive book for learning fundamental Bayesian approaches and practical techniques."
Sadaoki Furui, Tokyo Institute of Technology
Review was not posted due to profanity×
- Date Published: July 2015
- format: Adobe eBook Reader
- isbn: 9781316355107
- contains: 59 b/w illus. 13 tables
- availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
Table of Contents
Part I. General Discussion:
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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