I am currently working on recreating the results of this paper. In the paper they describe a method for using CNN for features extraction, and have a acoustic model that is Dnn-hmm and pretrained using RBM.
Section III subsection A states different ways the input data can be represented. I decided to vertically stack the spectrum plots of the static, delta and delta deltas.
The paper then describes how the network should be. They state that they use a convolutional network, but nothing about the structure of network?. Further more is the network always refered to as a convolutional ply? which i am sure i see any difference in compared to an ordinary network convolutional neural network (cnn).
The paper states this regarding the difference:
(from section III subsection B)
A convolution ply differs from a standard, fully connected hidden layer in two important aspects, however. First, each convolutional unit receives input only from a local area of the input. This means that each unit represents some features of a local region of the input. Second, the units of the convolution ply can themselves be organized into a number of feature maps, where all units in the same feature map share the same weights but receive input from different locations of the lower layer
Another thing i was wondering is whether the paper actually states how many output parameter is needed to feed the dnn-hmm acoustic model. I can't seem to decode the number of filters, filters sizes.. in general details of the network?