Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks

This paper introduces Connectionist Temporal Classification (CTC), a method that enables recurrent neural networks to directly label unsegmented sequence data without needing pre-processing or post-processing steps. CTC overcomes the alignment problem in tasks like speech recognition by allowing the network to predict a distribution over all possible label sequences, and then efficiently finds the most probable labeling using a forward-backward algorithm. This approach simplifies training and improves performance, making it suitable for various sequence labeling tasks.