% i am doing a speech recognition project; after performing MFC i got a almost a huge matrix 4903x1;(for a single sample).
Statistical terminology: A sample is a selected GROUP of data (typically not a single data point) .
Geometrical fact: The minimum number of points needed to define an I-dimensional input space is min(N) = I+1. Typically desire N >> I + 1
How many measurements do you have?
[ I N ] = size(input)
[ O N ] = size(target) % columns of eye(O) ; O = No. of speech categories
Neq = N*O % Number of training equations
What is the rank and condition number of your input matrix?
rankinput = rank(input)
condinput = cond(input)
%so i decided to downsample the by factor 19; which yields a 91x1 dimension matrix.
Better to obtain the 19 from MFCC ranking than just 'downsampling'. You may be choosing the worst 19 variables.
% I am using 91 neurons in hidden layer (nprtool i tried to keep the input matrix dim. less than 100
H = 91 is probably very excessive. See below
% Now my queries is: 1) Is it effecting my network performance for speech recognition?; because my network is not giving good result for untrained or testing speech data.
Most likely
%2)How many neurons should be used in hidden layers in relation to input data dimension.
It depends on the training algorithm. If you are not using validation set stopping or regularization via trainbr it is wise to have many more training equations than unknown weights to be estimated. For an I-H-O node topology
Neq >= Nw % Required ( H <= (Neq-O)/(I+O+1) )
Neq >> Nw % Desired ( H << (Neq-O)/(I+O+1) )
Nw = (I+1)*H+(H+1) = O+(I+O+1)*H % No. of estimated weights
% 3)what is the difference between weight layer and node layer?
There are 3 node layers: input, hidden and output
There are 2 weight layers: input-to-hidden and hidden-to-output
Any mention of layers in MATLLAB documentation refers to weight layers.
Hope this helps.
Greg
