I am currently studying this paper and are trying to understand what exactly the input and output shape is. The paper describes an acoustic model consisting of using cnn-hmm as the acoustic model. The input is a image of mel-log filter energies visualised as spectograms. The paper describes a method for phone recognition in which (as far I understand) applying a CNN on these spectograms with a limited weight sharing scheme should be beneficial for phone recognition.
The input shape as far i understand, is 9-15 frames, which seem a bit confusing, as they don't consider number of phonemes a utterance may have, or the length of them, but simply just "choose" a number of frames to operate with.. The number doesn't seem to be connected with the output in any way - or am I misinterpreting something?
For the output
We used 183 target class labels, i.e., 3 states for each HMM of 61 phones. After decoding, the original 61 phone classes were mapped to a set of 39 classes as in  for final scoring. In our experiments, a bigram language model over phones, estimated from the training set, was used in decoding. To prepare the ANN targets, a mono-phone HMM model was trained on the training data set, and it was used to generate state-level labels based on forced alignment.
So the output is divided in to 183 classes, being mapped into HMM's with 3 states for each 61 phonemes, and the ANN target (As I see it target = posterior probability) by training a monophone hmm with forced alignment. I am not sure I understand this process. If the ANN targets are those the CNN should aim/regress to and at the end classify the state based on, Why then process the input?.. why not make a simple DNN that does the regression/classification?
It looks like the improvement lies in the use of forced alignment here, and only on monophone? where is the improvement?
And again how I am supposed to link the input shape and the output shape based this? This would require the audio files to have a certain length, the length of the audio is never specified, so I am assuming that this is not the case.