Bayesian speech and language processing

Shinji Watanabe, Jen Tzung Chien

研究成果: Book

46 被引用数 (Scopus)

抄録

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.

本文言語English
出版社Cambridge University Press
ページ数424
ISBN(電子版)9781107295360
ISBN(印刷版)9781107055575
DOI
出版ステータスPublished - 2015 1月 1
外部発表はい

ASJC Scopus subject areas

  • 工学(全般)
  • コンピュータ サイエンス(全般)

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