Advances in Speech Recognition, Amy Neustein

Автор: Chin-Hui Lee; Frank K. Soong; Kuldip K. Paliwal
Название: Automatic Speech and Speaker Recognition
ISBN: 1461285909 ISBN-13(EAN): 9781461285908
Издательство: Springer
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Цена: 243800.00 T
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Описание: Research in the field of automatic speech and speaker recognition has made a number of significant advances in the last two decades, influenced by advances in signal processing, algorithms, architectures, and hardware.

Автор: Sabu M. Thampi; Alexander Gelbukh; Jayanta Mukhopa
Название: Advances in Signal Processing and Intelligent Recognition Systems
ISBN: 3319049593 ISBN-13(EAN): 9783319049595
Издательство: Springer
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Цена: 214280.00 T
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Описание: This edited volume contains a selection of refereed and revised papers originally presented at the International Symposium on Signal Processing and Intelligent Recognition Systems (SIRS-2014), March 13-15, 2014, Trivandrum, India.

Автор: Sabu M. Thampi; Sanghamitra Bandyopadhyay; Sri Kri
Название: Advances in Signal Processing and Intelligent Recognition Systems
ISBN: 3319286560 ISBN-13(EAN): 9783319286563
Издательство: Springer
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Цена: 243800.00 T
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Описание: This Edited Volumecontains a selection of refereed and revised papers originally presented at thesecond International Symposium on Signal Processing and Intelligent RecognitionSystems (SIRS-2015), December 16-19, 2015, Trivandrum, India.

Автор: Sabu M. Thampi; Sri Krishnan; Juan Manuel Corchado
Название: Advances in Signal Processing and Intelligent Recognition Systems
ISBN: 3319679333 ISBN-13(EAN): 9783319679334
Издательство: Springer
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Цена: 186330.00 T
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Описание: This Edited Volume gathers a selection of refereed and revised papers originally presented at the Third International Symposium on Signal Processing and Intelligent Recognition Systems (SIRS`17), held on September 13-16, 2017 in Manipal, India.

Автор: Donaj
Название: Language Modeling for Automatic Speech Recognition of Inflective Languages
ISBN: 3319416057 ISBN-13(EAN): 9783319416052
Издательство: Springer
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Цена: 51230.00 T
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Описание: This book covers language modeling and automatic speech recognition for inflective languages (e.g. Slavic languages), which represent roughly half of the languages spoken in Europe. These languages do not perform as well as English in speech recognition systems and it is therefore harder to develop an application with sufficient quality for the end user. The authors describe the most important language features for the development of a speech recognition system. This is then presented through the analysis of errors in the system and the development of language models and their inclusion in speech recognition systems, which specifically address the errors that are relevant for targeted applications. The error analysis is done with regard to morphological characteristics of the word in the recognized sentences. The book is oriented towards speech recognition with large vocabularies and continuous and even spontaneous speech. Today such applications work with a rather small number of languages compared to the number of spoken languages.

Автор: K. Church
Название: Phonological Parsing in Speech Recognition
ISBN: 146129200X ISBN-13(EAN): 9781461292005
Издательство: Springer
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Цена: 104480.00 T
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Описание: is typically realized with a heavily aspirated strong burst at the beginning of a syllable as in the word Tom, but without a burst at the end of a syllable in a word like cat. schwa vowels could be the first vowel in a word like `about` or the surface realization of almost any English vowel appearing in a sufficiently destressed word.

Автор: A. Acero
Название: Acoustical and Environmental Robustness in Automatic Speech Recognition
ISBN: 1461363667 ISBN-13(EAN): 9781461363668
Издательство: Springer
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Цена: 130610.00 T
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Описание: The need for automatic speech recognition systems to be robust with respect to changes in their acoustical environment has become more widely appreciated in recent years, as more systems are finding their way into practical applications.

Автор: Dong Yu; Li Deng
Название: Automatic Speech Recognition
ISBN: 1447157788 ISBN-13(EAN): 9781447157786
Издательство: Springer
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Цена: 130430.00 T
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Описание: This book reviews past and present work on discriminative and hierarchical models for both acoustic and language modeling. It also analyzes the research direction and trends towards establishing future-generation speech recognition.

Автор: K. Sreenivasa Rao; Manjunath K E
Название: Speech Recognition Using Articulatory and Excitation Source Features
ISBN: 3319492195 ISBN-13(EAN): 9783319492193
Издательство: Springer
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Цена: 51230.00 T
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Описание: Each chapter provides the motivation for exploring the specific feature for SR task, discusses the methods to extract those features, and finally suggests appropriate models to capture the sound unit specific knowledge from the proposed features.

Автор: Shinji Watanabe; Marc Delcroix; Florian Metze; Joh
Название: New Era for Robust Speech Recognition
ISBN: 3319646796 ISBN-13(EAN): 9783319646794
Издательство: Springer
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Цена: 139750.00 T
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Описание: It provides insights and detailed descriptions of some of the new concepts and key technologies in the field, including novel architectures for speech enhancement, microphone arrays, robust features, acoustic model adaptation, training data augmentation, and training criteria.

Автор: Mohamed Elmahdy; Rainer Gruhn; Wolfgang Minker
Название: Novel Techniques for Dialectal Arabic Speech Recognition
ISBN: 1489999450 ISBN-13(EAN): 9781489999450
Издательство: Springer
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Цена: 104480.00 T
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Описание: As speech resources for dialectal Arabic speech recognition are very sparse, this book will be widely welcomed, using Egyptian colloquial Arabic (ECA) as a typical dialect and providing a high-quality ECA speech corpus with accurate phonetic transcription.

Автор: Dorothea Kolossa; Reinhold Haeb-Umbach
Название: Robust Speech Recognition of Uncertain or Missing Data
ISBN: 3642438687 ISBN-13(EAN): 9783642438684
Издательство: Springer
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Цена: 113180.00 T
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Описание: This book presents the state of the art in recognition in the presence of uncertainty, offering examples that utilize uncertainty information for noise robustness, reverberation robustness, simultaneous recognition of multiple speech signals, and audiovisual speech recognition.