I've been looking into Recurrent Neural Networks, but I don't understand what the architecture of a neural network would look like when the output length is not necessarily fixed.
It seems like most networks I've read descriptions of require the output length to be equal to the input length or at least a fixed size. But how would you do something like convert a word to the string of corresponding phonemes?
The string of phonemes might be longer or shorter than the original word. I know you could sequence in the input characters using 8 input nodes (bitcode of the character) in a recurrent network, provided there's a loop in the network, but is this a common pattern for the output stream as well? Can you let the network provide provide something like a 'stop codon'?
I suppose a lot of practical networks, like those that do speech synthesis should have an output that is not fixed in length. How do people deal with that?