Connectionist Temporal Classification
Collapse alignment paths and sum their probabilities without needing presegmented labels.
Free theory articleCollapse alignment paths and sum their probabilities without needing presegmented labels.
Concept & interpretation
Connectionist Temporal Classification (CTC) is a scoring function used to train sequence-to-sequence models, such as recurrent neural networks for speech recognition, without requiring pre-segmented training data. It introduces a special "blank" symbol and allows identical consecutive labels to be merged.
Experiment
Let's explore how the CTC probability changes with target length:
- First, ensure the Frames is set to "Uniform two".
- Next, select the Target to be "AA".
- Observe that the target probability drops to exactly 0, and the Negative Log Likelihood becomes Infinite, showing that some targets are impossible to emit in fewer frames.
You can try this interactive module on the main site at: https://riano.app/module/connectionist-temporal-classification.