Bayesian speech and language processing

Shinji Watanabe, Jen Tzung Chien

Research output: Book/ReportBook

20 Citations (Scopus)

Abstract

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.

Original languageEnglish
PublisherCambridge University Press
Number of pages424
ISBN (Electronic)9781107295360
ISBN (Print)9781107055575
DOIs
Publication statusPublished - 2015 Jan 1
Externally publishedYes

Fingerprint

Learning systems
Processing
Hidden Markov models
Information retrieval
Speech recognition
Signal processing
Information systems
Students
Industry
Statistical Models

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science(all)

Cite this

Bayesian speech and language processing. / Watanabe, Shinji; Chien, Jen Tzung.

Cambridge University Press, 2015. 424 p.

Research output: Book/ReportBook

Watanabe, Shinji ; Chien, Jen Tzung. / Bayesian speech and language processing. Cambridge University Press, 2015. 424 p.
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