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Automatic speech recognition: a deep learning approach
This book summarizes the recent advancement in the field of automatic speech recognition with a focus on discriminative and hierarchical models. This will be the first automatic speech recognition book to include a comprehensive coverage of recent developments such as conditional random field and de...
Autores principales: | , |
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Lenguaje: | eng |
Publicado: |
Springer
2015
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.1007/978-1-4471-5779-3 http://cds.cern.ch/record/1973389 |
_version_ | 1780944916692598784 |
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author | Yu, Dong Deng, Li |
author_facet | Yu, Dong Deng, Li |
author_sort | Yu, Dong |
collection | CERN |
description | This book summarizes the recent advancement in the field of automatic speech recognition with a focus on discriminative and hierarchical models. This will be the first automatic speech recognition book to include a comprehensive coverage of recent developments such as conditional random field and deep learning techniques. It presents insights and theoretical foundation of a series of recent models such as conditional random field, semi-Markov and hidden conditional random field, deep neural network, deep belief network, and deep stacking models for sequential learning. It also discusses practical considerations of using these models in both acoustic and language modeling for continuous speech recognition. |
id | cern-1973389 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2015 |
publisher | Springer |
record_format | invenio |
spelling | cern-19733892021-04-21T20:42:22Zdoi:10.1007/978-1-4471-5779-3http://cds.cern.ch/record/1973389engYu, DongDeng, LiAutomatic speech recognition: a deep learning approachEngineeringThis book summarizes the recent advancement in the field of automatic speech recognition with a focus on discriminative and hierarchical models. This will be the first automatic speech recognition book to include a comprehensive coverage of recent developments such as conditional random field and deep learning techniques. It presents insights and theoretical foundation of a series of recent models such as conditional random field, semi-Markov and hidden conditional random field, deep neural network, deep belief network, and deep stacking models for sequential learning. It also discusses practical considerations of using these models in both acoustic and language modeling for continuous speech recognition.Springeroai:cds.cern.ch:19733892015 |
spellingShingle | Engineering Yu, Dong Deng, Li Automatic speech recognition: a deep learning approach |
title | Automatic speech recognition: a deep learning approach |
title_full | Automatic speech recognition: a deep learning approach |
title_fullStr | Automatic speech recognition: a deep learning approach |
title_full_unstemmed | Automatic speech recognition: a deep learning approach |
title_short | Automatic speech recognition: a deep learning approach |
title_sort | automatic speech recognition: a deep learning approach |
topic | Engineering |
url | https://dx.doi.org/10.1007/978-1-4471-5779-3 http://cds.cern.ch/record/1973389 |
work_keys_str_mv | AT yudong automaticspeechrecognitionadeeplearningapproach AT dengli automaticspeechrecognitionadeeplearningapproach |