Supervised Sequence Labelling with Recurrent Neural Networks (Hardcover)

Alex Graves

ISBN 10: 3642247962 / ISBN 13: 9783642247965

Published by Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2012

About this Item

Hardcover. Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning toolsrobust to input noise and distortion, able to exploit long-range contextual informationthat would seem ideally suited to such problems. However their role in large-scale sequence labelling systems has so far been auxiliary. 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. Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9783642247965

Bibliographic Details

• Title: Supervised Sequence Labelling with Recurrent Neural Networks (Hardcover)
• Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin
• Publication Date: 2012
• Language: English
• ISBN 10: 3642247962
• ISBN 13: 9783642247965
• Binding: Hardcover
• Condition: new

About this title

Synopsis

Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools―robust to input noise and distortion, able to exploit long-range contextual information―that would seem ideally suited to such problems. However their role in large-scale sequence labelling systems has so far been auxiliary. 

 

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.