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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5.1 Data
- What data and features were used in the CTC experiments?TIMIT phonetic labelling with 61 phonemes (4620 training, 1680 test utterances, 5% of training for validation). Audio was cut into 10 ms frames with 5 ms overlap, giving 12 MFCCs plus log-energy and their first derivatives: 26 coefficients per frame, normalized.
- Graves et al., 2006, Connectionist Temporal Classification (Proceedings of the 23rd International Conference on Machine Learning (ICML 2006)), §5.1, p. 374
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