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Connectionist Temporal Classification: Labelling ...

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.

A CTC network has a softmax output layer (Bridle, 1990) with one more unit than there are labels in L. The activations of the first |L| units are interpreted as the probabilities of observing the corresponding labels at particular times. The activation of the extra unit is the probability of observing a ‘blank’, or no label.

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