I seem to have some problems understandind how the model described in this paper has been designed
This is what is written about the model dimension..
...In these experiments we used one convolution ply, one poolingply and two fully connected hidden layers on the top. The fullyconnected layers had 1000 units in each. The convolution andpooling parameters were: pooling size of 6, shift size of 2,filtersize of 8, 150 feature maps for FWS..
So according to ^ does the model consist of
Input being the 150 feature maps (each with shape (8,3)
Covolution being 1d as kernel size is 8
and pooling is with size 6 and stride 2.
What was expected of output would be a shape of (1,"number of filters), but what i get is (14,"number of filters)
Which I understand why i get, but I don't understand how the paper suggest this can give an output shape of (1,"number of filters")
when using 100 filters I get these outputs from each layer
convolution1d give me (33,100)
Why i expect the output to be 1 instead of 14
The model is supposed to recognise phones, it takes in a 50 frames (150 including deltas) as input, these being a context frame, meaning that these are used as support to detect one single frame... That usually why context windows are used.