Connectionist temporal classification using segmented labeled sequence data

A classification training system comprising:

• a neural network for use in a classification of input data;
• a training dataset providing segmented labeled training data;
• and
• a classification training module operable to train the neural network using the segmented labeled training data, the classification training module comprising a forward pass processing module, and a backward pass processing module;
• wherein the forward pass processing module is operable to train the neural network by generating neural network outputs for the training data using weights and bias for the neural network;
• and
• wherein the backward pass processing module is operable to train the neural network by updating the weights and biases in a backward pass, wherein the backward pass module is operable to:
• obtain Region of Target (ROT) information from the segmented, labeled training data;
• generate a forward-backward masking based on the ROT information, the forward-backward masking placing at least one restriction on a neural network output path;
• compute modified forward and backward variables based on the neural network outputs and the forward-backward masking;
• and
• update the weights and biases of the neural network.