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.

這本全面的指南將教你如何系統地應用貝葉斯機器學習技術來解決語音和語言處理中的各種問題。詳細介紹了一系列統計模型，從隱藏馬可夫模型到高斯混合模型、n-gram模型和潛在主題模型，以及自動語音識別、語者驗證和信息檢索等應用。提供了基於MAP、Evidence、漸進、VB和MCMC逼近的近似貝葉斯推斷，以及計算的完整推導、有用的符號、公式和規則。作者們解決了直接應用的困難，並提供了詳細的示例和案例研究，以展示如何成功地使用實用的貝葉斯推斷方法來提高信息系統的性能。這是一個對於從事機器學習、信號處理和語音語言處理的學生、研究人員和業界從業人員來說無價的資源。