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Connectionist Temporal Classification: Labelling UnsegmentedSequence Data with Recurrent Neural NetworksAlex Fern urgen Dalle Molle di Studi sull Intelligenza Artificiale (IDSIA), Galleria 2, 6928 Manno-Lugano, Switzerland2 Technische Universit at M unchen (TUM), Boltzmannstr. 3, 85748 Garching, Munich, GermanyAbstractMany real-world sequence learning tasks re-quire the prediction of sequences of labelsfrom noisy, unsegmented input data. Inspeech recognition, for example, an acousticsignal is transcribed into words or sub-wordunits. Recurrent neural networks (RNNs) arepowerful sequence learners that would seemwell suited to such tasks. However, becausethey require pre-segmented training data,and post-processing to transform their out-puts into label sequences, their applicabilityhas so far been limited.
Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks Alex Graves1 alex@idsia.ch Santiago Fern´andez1 santiago@idsia.ch Faustino Gomez1 tino@idsia.ch Jurgen¨ Schmidhuber1,2 juergen@idsia.ch 1 Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA), Galleria 2, 6928 Manno-Lugano, Switzerland
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