2-AIN-506 a 2-AIN-252: Seminár z bioinformatiky (2) a (4)
Leto 2018

Alex Graves, Santiago Fernandez, Faustino Gomez, Jurgen Schmidhuber. Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In ICML '06 Proceedings of the 23rd international conference on Machine learning, pp. 369-376, 2006.

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Many real-world sequence learning tasks require the prediction of sequences 
of labels from noisy, unsegmented input data. In speech recognition, for 
example, an acoustic signal is transcribed into words or sub-word units. 
Recurrent neural networks (RNNs) are powerful sequence learners that would 
seem well suited to such tasks. However, because they require pre-segmented 
training data, and post-processing to transform their outputs into label 
sequences, their applicability has so far been limited. This paper presents 
a novel method for training RNNs to label unsegmented sequences directly, 
thereby solving both problems. An experiment on the TIMIT speech corpus 
demonstrates its advantages over both a baseline HMM and a hybrid HMM-RNN.