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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. This paper presents anovel method for training RNNs to label un-segmented sequences directly, thereby solv-ing both problems.
temporal classifiers, giving directions for future work, and the paper concludes with section 7. 2. Temporal Classification Let S be a set of training examples drawn from a fixed distribution D X×Z. The input space X = (Rm)∗ is the set of all sequences of m dimensional real val-ued vectors. The target space Z = L∗ is the set
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