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Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks
This paper presents a novel method for training RNNs to label unsegmented sequences directly, thereby solving both problems of sequence learning and post-processing.
A Novel Connectionist System for Unconstrained Handwriting Recognition
- A. Graves, M. Liwicki, Santiago Fernández, Roman Bertolami, H. Bunke, J. Schmidhuber
- Computer ScienceIEEE Transactions on Pattern Analysis and Machine…
- 1 May 2009
This paper proposes an alternative approach based on a novel type of recurrent neural network, specifically designed for sequence labeling tasks where the data is hard to segment and contains long-range bidirectional interdependencies, significantly outperforming a state-of-the-art HMM-based system.
Bidirectional LSTM Networks for Improved Phoneme Classification and Recognition
In this paper, two experiments on the TIMIT speech corpus with bidirectional and unidirectional Long Short Term Memory networks are carried out and it is found that a hybrid BLSTM-HMM system improves on an equivalent traditional HMM system.
Multi-dimensional Recurrent Neural Networks
Multi-dimensional recurrent neural networks are introduced, thereby extending the potential applicability of RNNs to vision, video processing, medical imaging and many other areas, while avoiding the scaling problems that have plagued other multi-dimensional models.
Multidimensional Recurrent Neural Networks
An Application of Recurrent Neural Networks to Discriminative Keyword Spotting
A discriminative keyword spotting system based on recurrent neural networks only, that uses information from long time spans to estimate word-level posterior probabilities of sub-word units, is presented.
Sequence Labelling in Structured Domains with Hierarchical Recurrent Neural Networks
This paper presents a hierarchical system, based on the connectionist temporal classification algorithm, for labelling unsegmented sequential data at multiple scales with recurrent neural networks only and shows that the system outperforms hidden Markov models, while making fewer assumptions about the domain.
Unconstrained On-line Handwriting Recognition with Recurrent Neural Networks
- A. Graves, Santiago Fernández, M. Liwicki, H. Bunke, J. Schmidhuber
- Computer ScienceNIPS
- 3 December 2007
A system capable of directly transcribing raw online handwriting data is described, consisting of an advanced recurrent neural network with an output layer designed for sequence labelling, combined with a probabilistic language model.
Application of the metabolic enthalpy change in studies of soil microbial activity
Phoneme recognition in TIMIT with BLSTM-CTC
The performance of a recurrent neural network is compared with the best results published so far on phoneme recognition in the TIMIT database and a single recurrent network is applied to the same task.