Connectionist Temporal Classification
Connectionist Temporal Classification is a discriminative machine learning framework within the family of sequence-to-sequence models that enables end-to-end training of neural networks for temporal pattern recognition by directly optimizing the alignment between input sequences and output label sequences without requiring prior segmentation.
307
Publications
34.5K
Citations
913
Authors
239
Institutions
Publications per year
2017–2026
256
Parents
307
Speech recognition with deep recurrent neural networks
Alex Graves, Abdelrahman Mohamed, Geoffrey E. Hinton · 2013 · 8.8K citations
Connectionist temporal classification
Alex Graves, Santiago Fernández, Faustino Gomez et al. · 2006 · 5.4K citations
1–5 of 307
913
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239
Google (United States)
Mountain View, United States
14 h-index
75 publications
7.5K citations
Carnegie Mellon University
Pittsburgh, United States
13 h-index
39 publications
5.6K citations

Johns Hopkins University
Baltimore, United States
13 h-index
27 publications
1.3K citations
Mitsubishi Electric (United States)
Cypress, United States
11 h-index
27 publications
4.4K citations

Chinese Academy of Sciences
Beijing, China
11 h-index
35 publications
1.4K citations
Microsoft (United States)
Redmond, United States
10 h-index
46 publications
3.2K citations
University of Chinese Academy of Sciences
Beijing, China
9 h-index
25 publications
1.1K citations
Tsinghua University
Beijing, China
8 h-index
17 publications
1.4K citations
1–8 of 239
Repository · 41 publications · 1.9K citations
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TASLP · 10 publications · 270 citations
10
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8 publications · 744 citations
8
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8 publications · 310 citations
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Conference · 5 publications · 112 citations
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