2012 | OriginalPaper | Chapter
Connectionist Temporal Classification
Author : Alex Graves
Published in: Supervised Sequence Labelling with Recurrent Neural Networks
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
This chapter introduces the
connectionist temporal classification
(CTC) output layer for recurrent neural networks (Graves et al., 2006). As its name suggests, CTC was specifically designed for temporal classification tasks; that is, for sequence labelling problems where the alignment between the inputs and the target labels is unknown. Unlike the hybrid approach described in the previous chapter, CTC models all aspects of the sequence with a single neural network, and does not require the network to be combined with a hidden Markov model. It also does not require presegmented training data, or external postprocessing to extract the label sequence from the network outputs. Experiments on speech and handwriting recognition show that a BLSTM network with a CTC output layer is an effective sequence labeller, generally outperforming standardHMMsandHMM-neural network hybrids, as well asmore recent sequence labelling algorithms such as large margin HMMs (Sha and Saul, 2006) and conditional random fields (Lafferty et al., 2001).