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 m...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2015
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| Schlagworte: | |
| Online-Zugang: | BSB01 FHN01 Volltext |
| Zusammenfassung: | 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 |
| Beschreibung: | Title from publisher's bibliographic system (viewed on 05 Oct 2015) |
| Beschreibung: | 1 online resource (xxi, 424 pages) |
| ISBN: | 9781107295360 |
| DOI: | 10.1017/CBO9781107295360 |
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| 100 | 1 | |a Watanabe, Shinji |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Bayesian speech and language processing |c Shinji Watanabe, Jen-Tzung Chien |
| 246 | 1 | 3 | |a Bayesian speech & language processing |
| 264 | 1 | |a Cambridge |b Cambridge University Press |c 2015 | |
| 300 | |a 1 online resource (xxi, 424 pages) | ||
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| 505 | 8 | |a Machine generated contents note: 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 | |
| 520 | |a 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 | ||
| 650 | 4 | |a Sprache | |
| 650 | 4 | |a Language and languages / Study and teaching / Statistical method | |
| 650 | 4 | |a Bayesian statistical decision theory | |
| 700 | 1 | |a Chien, Jen-Tzung |e Sonstige |4 oth | |
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Datensatz im Suchindex
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| author | Watanabe, Shinji |
| author_facet | Watanabe, Shinji |
| author_role | aut |
| author_sort | Watanabe, Shinji |
| author_variant | s w sw |
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| bvnumber | BV043940387 |
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| contents | Machine generated contents note: 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 |
| ctrlnum | (ZDB-20-CBO)CR9781107295360 (OCoLC)992910224 (DE-599)BVBBV043940387 |
| dewey-full | 410.1/51 |
| dewey-hundreds | 400 - Language |
| dewey-ones | 410 - Linguistics |
| dewey-raw | 410.1/51 |
| dewey-search | 410.1/51 |
| dewey-sort | 3410.1 251 |
| dewey-tens | 410 - Linguistics |
| discipline | Sprachwissenschaft |
| doi_str_mv | 10.1017/CBO9781107295360 |
| format | Electronic eBook |
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| id | DE-604.BV043940387 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:39:13Z |
| institution | BVB |
| isbn | 9781107295360 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029349357 |
| oclc_num | 992910224 |
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| physical | 1 online resource (xxi, 424 pages) |
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| publishDate | 2015 |
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| publisher | Cambridge University Press |
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| spelling | Watanabe, Shinji Verfasser aut Bayesian speech and language processing Shinji Watanabe, Jen-Tzung Chien Bayesian speech & language processing Cambridge Cambridge University Press 2015 1 online resource (xxi, 424 pages) txt rdacontent c rdamedia cr rdacarrier Title from publisher's bibliographic system (viewed on 05 Oct 2015) Machine generated contents note: 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 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 Sprache Language and languages / Study and teaching / Statistical method Bayesian statistical decision theory Chien, Jen-Tzung Sonstige oth Erscheint auch als Druckausgabe 978-1-107-05557-5 https://doi.org/10.1017/CBO9781107295360 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Watanabe, Shinji Bayesian speech and language processing Machine generated contents note: 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 Sprache Language and languages / Study and teaching / Statistical method Bayesian statistical decision theory |
| title | Bayesian speech and language processing |
| title_alt | Bayesian speech & language processing |
| title_auth | Bayesian speech and language processing |
| title_exact_search | Bayesian speech and language processing |
| title_full | Bayesian speech and language processing Shinji Watanabe, Jen-Tzung Chien |
| title_fullStr | Bayesian speech and language processing Shinji Watanabe, Jen-Tzung Chien |
| title_full_unstemmed | Bayesian speech and language processing Shinji Watanabe, Jen-Tzung Chien |
| title_short | Bayesian speech and language processing |
| title_sort | bayesian speech and language processing |
| topic | Sprache Language and languages / Study and teaching / Statistical method Bayesian statistical decision theory |
| topic_facet | Sprache Language and languages / Study and teaching / Statistical method Bayesian statistical decision theory |
| url | https://doi.org/10.1017/CBO9781107295360 |
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