Notice bibliographique
- Notice
Type(s) de contenu et mode(s) de consultation : Texte noté. Image fixe : sans médiation
Auteur(s) : Watanabe, Shinji
Chien, Jen-Tzung
Titre(s) : Bayesian speech and language processing [Texte imprimé] / Shinji Watanabe, ... Jen-Tzung Chien, ...
Publication : Cambridge : Cambridge university press, cop. 2015
Description matérielle : xxi, 424 pages ; 26 cm
Comprend : 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.
Note(s) : Bibliogr. p. 405-421. Index
"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" ; "In general, speech
and language processing involves extensive knowledge of statistical models. The acoustic
model using hidden Markov models and language model using n-grams are mainly introduced.
Both acoustic and language models are important parts of modern speech recognition
systems where the learned models from real-world data are full of complexity, ambiguity
and uncertainty. The uncertainty modeling is crucial to tackle the lack of robustness
for speech and language processing"
Sujet(s) : Langues -- Étude et enseignement -- Méthodes statistiques
Statistique bayésienne
Identifiants, prix et caractéristiques : ISBN 9781107055575. - ISBN 1107055571 (rel.)
Identifiant de la notice : ark:/12148/cb443272455
Notice n° :
FRBNF44327245
(notice reprise d'un réservoir extérieur)