Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks
Abstract
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.
Cite
Text
Graves et al. "Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks." International Conference on Machine Learning, 2006. doi:10.1145/1143844.1143891Markdown
[Graves et al. "Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks." International Conference on Machine Learning, 2006.](https://mlanthology.org/icml/2006/graves2006icml-connectionist/) doi:10.1145/1143844.1143891BibTeX
@inproceedings{graves2006icml-connectionist,
title = {{Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks}},
author = {Graves, Alex and Fernández, Santiago and Gomez, Faustino J. and Schmidhuber, Jürgen},
booktitle = {International Conference on Machine Learning},
year = {2006},
pages = {369-376},
doi = {10.1145/1143844.1143891},
url = {https://mlanthology.org/icml/2006/graves2006icml-connectionist/}
}