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The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal. Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional recurrent neural networks extend the framework in a natural way to data with more than one spatio-temporal dimension, such as images and videos. Thirdly, the use of hierarchical subsampling makes it feasible to apply the framework to very large or high resolution sequences, such as raw audio or video.
Experimental validation is provided by state-of-the-art results in speech and handwriting recognition.
The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal. Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional recurrent neural networks extend the framework in a natural way to data with more than one spatio-temporal dimension, such as images and videos. Thirdly, the use of hierarchical subsampling makes it feasible to apply the framework to very large or high resolution sequences, such as raw audio or video.
Experimental validation is provided by state-of-the-art results in speech and handwriting recognition.
Recent research in Supervised Sequence Labelling with Recurrent Neural Networks
New results in a hot topic
Written by leading experts
| Erscheinungsjahr: | 2014 |
|---|---|
| Fachbereich: | Technik allgemein |
| Genre: | Mathematik, Medizin, Naturwissenschaften, Technik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: |
xiv
146 S. |
| ISBN-13: | 9783642432187 |
| ISBN-10: | 3642432182 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: | Graves, Alex |
| Hersteller: |
Springer-Verlag GmbH
Springer Berlin Heidelberg |
| Verantwortliche Person für die EU: | Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com |
| Maße: | 235 x 155 x 9 mm |
| Von/Mit: | Alex Graves |
| Erscheinungsdatum: | 13.04.2014 |
| Gewicht: | 0,254 kg |
Recent research in Supervised Sequence Labelling with Recurrent Neural Networks
New results in a hot topic
Written by leading experts
| Erscheinungsjahr: | 2014 |
|---|---|
| Fachbereich: | Technik allgemein |
| Genre: | Mathematik, Medizin, Naturwissenschaften, Technik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: |
xiv
146 S. |
| ISBN-13: | 9783642432187 |
| ISBN-10: | 3642432182 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: | Graves, Alex |
| Hersteller: |
Springer-Verlag GmbH
Springer Berlin Heidelberg |
| Verantwortliche Person für die EU: | Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com |
| Maße: | 235 x 155 x 9 mm |
| Von/Mit: | Alex Graves |
| Erscheinungsdatum: | 13.04.2014 |
| Gewicht: | 0,254 kg |
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