Connectionist temporal classification

Connectionist temporal classification (CTC) is a type of neural network output and associated scoring function, for training recurrent neural networks (RNNs) such as LSTM networks to tackle sequence problems where the timing is variable. It can be used for tasks like on-line handwriting recognition[1] or recognizing phonemes in speech audio. CTC refers to the outputs and scoring, and is independent of the underlying neural network structure. It was introduced in 2006.[2]

The input is a sequence of observations, and the outputs are a sequence of labels, which can include blank outputs. The difficulty of training comes from there being many more observations than there are labels. For example, in speech audio there can be multiple time slices which correspond to a single phoneme. Since we don't know the alignment of the observed sequence with the target labels we predict a probability distribution at each time step.[3] A CTC network has a continuous output (e.g. softmax), which is fitted through training to model the probability of a label. CTC does not attempt to learn boundaries and timings: Label sequences are considered equivalent if they differ only in alignment, ignoring blanks. Equivalent label sequences can occur in many ways – which makes scoring a non-trivial task, but there is an efficient forward–backward algorithm for that.

CTC scores can then be used with the back-propagation algorithm to update the neural network weights.

Alternative approaches to a CTC-fitted neural network include a hidden Markov model (HMM).

In 2009, a Connectionist Temporal Classification (CTC)-trained LSTM network was the first RNN to win pattern recognition contests when it won several competitions in connected handwriting recognition.[4][5]

In 2014, the Chinese company Baidu used CTC-trained RNNs to break the 2S09 Switchboard Hub5'00 speech recognition dataset[6] benchmark without using any traditional speech processing methods.[7]

In 2015, it was used in Google voice search and dictation on Android devices.[8]

References

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  1. ^ Liwicki, Marcus; Graves, Alex; Bunke, Horst; Schmidhuber, Jürgen (2007). "A novel approach to on-line handwriting recognition based on bidirectional long short-term memory networks". In Proceedings of the 9th International Conference on Document Analysis and Recognition, ICDAR 2007. CiteSeerX 10.1.1.139.5852.
  2. ^ Graves, Alex; Fernández, Santiago; Gomez, Faustino; Schmidhuber, Juergen (2006). "Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks". Proceedings of the International Conference on Machine Learning, ICML 2006: 369–376. CiteSeerX 10.1.1.75.6306.
  3. ^ Hannun, Awni (27 November 2017). "Sequence Modeling with CTC". Distill. 2 (11). arXiv:1508.01211. doi:10.23915/distill.00008. ISSN 2476-0757.
  4. ^ Schmidhuber, Jürgen (January 2015). "Deep Learning in Neural Networks: An Overview". Neural Networks. 61: 85–117. arXiv:1404.7828. doi:10.1016/j.neunet.2014.09.003. PMID 25462637. S2CID 11715509.
  5. ^ Graves, Alex; Schmidhuber, Jürgen (2009). "Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks". In Koller, D.; Schuurmans, D.; Bengio, Y.; Bottou, L. (eds.). Advances in Neural Information Processing Systems. Vol. 21. Neural Information Processing Systems (NIPS) Foundation. pp. 545–552.
  6. ^ "2000 HUB5 English Evaluation Speech - Linguistic Data Consortium". catalog.ldc.upenn.edu.
  7. ^ Hannun, Awni; Case, Carl; Casper, Jared; Catanzaro, Bryan; Diamos, Greg; Elsen, Erich; Prenger, Ryan; Satheesh, Sanjeev; Sengupta, Shubho (17 December 2014). "Deep Speech: Scaling up end-to-end speech recognition". arXiv:1412.5567 [cs.CL].
  8. ^ Sak, Haşim; Senior, Andrew; Rao, Kanishka; Beaufays, Françoise; Schalkwyk, Johan (September 2015). "Google voice search: faster and more accurate".
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