Bayesian Speech and Language Processing

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.

Table of Contents:
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.

About the Author :
Shinji Watanabe received his PhD from Waseda University in 2006. He has been a research scientist at NTT Communication Science Laboratories, a visiting scholar at Georgia Institute of Technology and a Senior Principal Member at Mitsubishi Electric Research Laboratories (MERL), as well as having been an Associate Editor of the IEEE Transactions on Audio Speech and Language Processing, and an elected member of the IEEE Speech and Language Processing Technical Committee. He has published more than 100 papers in journals and conferences, and received several awards including the best paper award from IEICE in 2003. Jen-Tzung Chien is with the Department of Electrical and Computer Engineering and the Department of Computer Science at the National Chiao Tung University, Taiwan, where he is now the University Chair Professor. He received the Distinguished Research Award from the Ministry of Science and Technology, Taiwan, and the Best Paper Award of the 2011 IEEE Automatic Speech Recognition and Understanding Workshop. He serves currently as an elected member of the IEEE Machine Learning for Signal Processing Technical Committee.

Review :
'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