Connectionist Temporal Classification
E736823
Connectionist Temporal Classification is a neural network training algorithm designed for sequence labeling tasks where input and output lengths differ and alignments are unknown, widely used in speech and handwriting recognition.
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
loss function
ⓘ
neural network training algorithm ⓘ sequence labeling method ⓘ |
| abbreviation | CTC NERFINISHED ⓘ |
| advantage |
enables end-to-end training
ⓘ
no need for pre-aligned training data ⓘ |
| assumes | conditional independence between output labels given network outputs ⓘ |
| basedOn | recurrent neural networks ⓘ |
| category | probabilistic sequence model ⓘ |
| comparedWith | HMM-based sequence labeling ⓘ |
| coreIdea | sums over all valid alignments between input and output sequences ⓘ |
| designedFor | sequence labeling tasks ⓘ |
| field |
deep learning
ⓘ
handwriting recognition ⓘ machine learning ⓘ speech recognition ⓘ |
| handles |
unsegmented input data
ⓘ
variable-length input sequences ⓘ variable-length output sequences ⓘ |
| hasComponent |
alignment paths
ⓘ
collapse function from paths to label sequences ⓘ |
| inspired | end-to-end ASR systems such as Deep Speech ⓘ |
| introducedBy |
Alex Graves
NERFINISHED
ⓘ
Faustino Gomez NERFINISHED ⓘ Jürgen Schmidhuber NERFINISHED ⓘ Santiago Fernández NERFINISHED ⓘ |
| lossFamily | negative log-likelihood loss ⓘ |
| objective | maximize log-likelihood of correct label sequence ⓘ |
| oftenUsedWith |
RNN acoustic models
ⓘ
bidirectional LSTM networks ⓘ |
| optimizationMethod | gradient-based optimization ⓘ |
| outputType | label sequence probabilities ⓘ |
| publicationYear | 2006 ⓘ |
| publishedIn | Proceedings of the 23rd International Conference on Machine Learning NERFINISHED ⓘ |
| relatedTo |
attention mechanisms
ⓘ
sequence-to-sequence models ⓘ |
| requires | a special blank label ⓘ |
| solves | alignment-free sequence labeling ⓘ |
| supports | training without frame-level alignments ⓘ |
| trainingSignal | sequence-level supervision ⓘ |
| usedIn |
end-to-end speech recognition
ⓘ
offline handwriting recognition ⓘ online handwriting recognition ⓘ optical character recognition ⓘ scene text recognition ⓘ sign language recognition ⓘ |
| uses |
dynamic programming
ⓘ
forward-backward algorithm ⓘ |
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.