Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks
Graves et al., 2006, Connectionist Temporal Classification (Proceedings of the 23rd International Conference on Machine Learning (ICML 2006))
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5.2 Experimental Setup
- Describe the CTC network setup on TIMIT.BLSTM with peepholes and forget gates, 100 blocks in each direction, a 62-unit softmax (61 phonemes plus blank), about 115k weights; online gradient descent with learning rate \(10^{-4}\), momentum 0.9, and Gaussian input noise (σ = 0.6) for generalization. The HMM baseline had over 900k parameters.
- Graves et al., 2006, Connectionist Temporal Classification (Proceedings of the 23rd International Conference on Machine Learning (ICML 2006)), §5.2, p. 375
Source
- Alex Graves, Santiago Fernández, Faustino Gomez and Jürgen Schmidhuber. 2006. Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks. In Proceedings of the 23rd International Conference on Machine Learning (ICML 2006), pp. 369–376. doi:10.1145/1143844.1143891