Python Deep Learning - Second Edition

CTC

Standard RNN objective functions are defined independently for each sequence step; each step outputs its own independent label classification. This means that training data must be perfectly aligned with the target labels. However, a global objective function that maximizes the probability of a full correct labeling can be devised. The idea is to interpret the network outputs as a conditional probability distribution over all possible labeling sequences, given the full input sequence. The network can then be used as a classifier by searching for the most probable labeling, given the input sequence.

Connectionist Temporal Classification (CTC) is an objective function that defines a distribution over all the alignments with all the output ...