Connectionist Temporal Classification (CTC) is a neural network output layer designed for sequence-to-sequence problems where the alignment between input and output sequences is unknown, such as in speech recognition. It allows the model to predict sequences of varying lengths by introducing a special 'blank' token, which enables the network to learn the optimal alignment between inputs and outputs during training.