Bayesian Speech and Language Processing

A practical and comprehensive guide on how to apply Bayesian machine learning techniques to solve speech and language processing problems.

Shinji Watanabe (Author), Jen-Tzung Chien (Author)

9781107055575, Cambridge University Press

Hardback, published 15 July 2015

445 pages, 59 b/w illus. 13 tables
25.5 x 18 x 2.4 cm, 1.03 kg

'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

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.

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.

Subject Areas: Communications engineering / telecommunications [TJK], Electronics & communications engineering [TJ], Probability & statistics [PBT]