Using Tensorflow's Connectionist Temporal Classification (CTC) implementation

See here for an example with bidirectional LSTM, CTC, and edit distance implementations, training a phoneme recognition model on the TIMIT corpus. If you train on that corpus's training set, you should be able to get phoneme error rates down to 20-25% after 120 epochs or so.

I'm trying to do the same thing. Here's what I found you may be interested in.

It was really hard to find the tutorial for CTC, but this example was helpful.

And for the blank label, CTC layer assumes that the blank index is num_classes - 1, so you need to provide an additional class for the blank label.

Also, CTC network performs softmax layer. In your code, RNN layer is connected to CTC loss layer. Output of RNN layer is internally activated, so you need to add one more hidden layer (it could be output layer) without activation function, then add CTC loss layer.