Neural Foundations · 2006
Connectionist Temporal Classification (CTC)
Graves et al. introduced Connectionist Temporal Classification, a loss function that lets a recurrent network be trained to map an input sequence to a shorter output label sequence without knowing the alignment between them, solving the need for pre-segmented, frame-by-frame labeled training data in tasks like speech recognition.
Editorial record
Plain-language summary
CTC adds a special 'blank' symbol and defines the probability of a target label sequence as the sum over all frame-level paths that collapse to it (by removing blanks and merging repeats), computed efficiently with a forward-backward dynamic-programming algorithm. This lets the network output a probability distribution over labels at every time step and be trained end-to-end on unsegmented pairs of input and target sequences. It removed the requirement for hand-aligned data and enabled direct recurrent-network sequence transcription in speech and handwriting recognition.
Source record
Provenance
- Record ID
- A-025
- Record created
- 2026-07-13
- Last reviewed
- 2026-07-14
- Record version
- 2
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